联邦学习

Clustered Federated Learning (CFL) mitigates statistical heterogeneity by grouping clients with similar data distributions. However, existing CFL frameworks predominantly rely on proxy metrics, such as gradient or data similarities, which collapse fine-grained class-level similarity into macroscopic client-level scores. This coarse-grained aggregation inherently masks critical class-wise conflicts, inadvertently exposing the federation to severe negative transfer. In this paper, we propose a fundamental paradigm shift from heuristic similarity to *explicit value attribution*. We introduce **FedCap**, a novel utility-driven CFL framework that explicitly quantifies class-wise marginal utilities to capture the true generalization impact of local updates. Guided by this attribution mechanism, FedCap employs a conflict-aware clustering algorithm to systematically eliminate intra-cluster negative transfer, while orchestrating safe, supply-demand-guided representation sharing across clusters. Crucially, we provide rigorous theoretical guarantees demonstrating the robustness of our underlying attribution mechanism against four major types of distribution shifts. Extensive experiments across diverse heterogeneity settings show that FedCap consistently achieves state-of-the-art accuracy. The code is available for anonymous access at https://anonymous.4open.science/r/FedCap-605E.

Heterogeneous Federated Learning (HFL) aims to enable collaboration among clients with diverse model architectures and non-IID data distributions, where direct parameter aggregation is often infeasible and prototype-based aggregation may suffer from semantic drift. Existing methods usually construct global references from client-side representations, making the alignment process vulnerable to local statistical bias and noisy or corrupted prototype updates. In this paper, we propose SATPL, a synthetic anchor-assisted prototype alignment framework that introduces externally generated synthetic anchors as auxiliary semantic priors for HFL. Instead of treating Large Language Models (LLMs) as infallible semantic oracles, SATPL uses LLM-generated class descriptions and generative models to construct reproducible reference prototypes for supervised tasks with known class semantics. Each client maps its heterogeneous local representation into a shared anchor space through a lightweight projection head, while an alignment-weighted aggregation rule assigns lower weights to prototypes that are highly inconsistent with the corresponding anchors. The blockchain component is used as an auditable recording layer for anchor commitments and aggregation metadata, rather than as the source of the statistical robustness guarantee. Experiments under architecture heterogeneity, label skew, and random prototype corruption show that SATPL improves accuracy, convergence stability, and robustness over representative HFL baselines. Additional analyses evaluate anchor quality, temperature sensitivity, and system overhead, clarifying both the benefits and limitations of synthetic-anchor-based alignment.

Medical visual question answering (VQA) and federated learning (FL) have gained prominence as critical methods for clinical artificial intelligence, with VQA supporting image-based diagnostic reasoning and FL allowing institutions to train models jointly without sharing sensitive patient data. However, existing approaches face substantial challenges in cross-modal vertical FL settings, where each participating institution possesses unimodal, modality-specific textual data without access to paired multimodal samples. To address this limitation, we introduce SynTeX-FL, a federated framework designed to enable cross-modal text transfer for medical VQA without sharing raw data. Moreover, SynTeX-FL applies a reconstruction-based text synthesis model that extracts clinically relevant semantics from medical images to generate cross-modal textual representations. Furthermore, the proposed method incorporates modality-specialized low-rank adaptation (LoRA) modules to enhance modality-aware representations for each imaging domain. During aggregation, the central server employs discriminator-based quality scores and source-aware weighting strategies to integrate heterogeneous client contributions. Extensive experiments on multimodal medical VQA benchmarks demonstrate that SynTeX-FL significantly outperforms current FL baselines, providing a robust and efficient solution for cross-modal reasoning in decentralized clinical environments.

Federated Learning (FL) is a promising paradigm for distributed machine learning. However, FL often suffers from degraded generalization performance due to the inconsistency between local and global optimization objectives and client-side overfitting. In this paper, we provide a global-update stability analysis framework as an analytical tool to study generalization error and derive the stability bounds of mainstream FL optimization algorithms under non-convex settings. Our analyses reveal how the number of global update steps, data heterogeneity, and update rules influence their stability. We observe that momentum-based FL acceleration methods do not improve stability. To address this issue, we propose FedSEMA, a new FL algorithm that couples global momentum with a gradient-difference corrected Nesterov Accelerated Gradient (NAG) scheme and a hybrid proximal term to enhance stability. This design ensures updates follow a globally consistent descent direction while retaining the benefits of acceleration. Theoretical analysis shows that FedSEMA achieves an improved stability upper bound on non-i.i.d. datasets in the non-convex settings. Extensive experiments on real-world datasets demonstrate that FedSEMA significantly outperforms multiple baseline methods under standard FL settings, achieving faster convergence and state-of-the-art performance.

Federated Prompt Tuning (FPT) adapts large vision--language models by freezing the pretrained backbone and optimizing only lightweight prompt parameters across clients. Although this design improves communication and parameter efficiency, it creates an overlooked security risk. Since the backbone is shared and frozen, a malicious client can induce backdoor behavior through the representation space while keeping its uploaded prompt updates close to benign ones. We propose \textbf{ShadowFPT}, a targeted backdoor attack that exploits this frozen-backbone attack surface. ShadowFPT first pretrains a learnable \emph{Shadow Trigger} against the frozen CLIP visual encoder, using either auxiliary public data or the malicious client's local data, so that triggered inputs are steered toward the target class in representation space. During federated prompt tuning, the malicious client adapts the trigger under the current global prompt and then optimizes its local prompt on both clean and triggered samples. Only prompt parameters are uploaded to the server, while the trigger and frozen encoders remain local. By shifting most of the attack burden from prompt updates to trigger-induced representation steering, ShadowFPT achieves targeted misclassification while preserving prompt-space stealthiness. Across multiple datasets, aggregation rules, and non-IID federated partitions, ShadowFPT increases the attack success rate from 19.81\% to 90.36\% in our main setting, while maintaining clean accuracy. It remains effective across textual, visual, and joint vision--language prompt tuning. These results identify frozen backbones as stealthy and underexplored backdoor surfaces in federated prompt tuning, suggesting that defenses based only on prompt-update anomaly detection are insufficient.

Federated intrusion detection must adapt to rare attacks from only a few labeled flows per client, while client distributions are highly non-IID and raw traffic cannot be centralized. Existing few-shot intrusion detectors typically construct class priors in centralized settings, and standard federated training does not provide an explicit mechanism for reusing cross-client class statistics during local low-shot adaptation. We propose a semantic-statistical prior bank for federated low-shot intrusion detection. Clients first participate in federated representation learning and submit protected class-wise moment statistics through secure aggregation, from which the server constructs a global prior bank without accessing raw traffic. During local episodic adaptation, label-phrase embeddings retrieve relevant priors, which are combined with scarce support statistics through a maximum a posteriori (MAP) shrinkage estimator in the feature space. The adapted class distributions are then used to train lightweight episode-specific classifiers on each client. Experiments on CICIDS2017 and CICDDoS2019 under 5-way 1-shot and 5-shot non-IID settings show consistent improvements over FedAvg and backbone-matched baselines, with the largest gains appearing in 1-shot and highly skewed client distributions. With ResNet1D, the proposed adaptation improves 1-shot F1 from 92.97 to 95.55 on CICIDS2017 and from 82.13 to 88.25 on CICDDoS2019.

Federated learning (FL) enables collaborative model training across distributed clients without sharing raw data, yet its scalability is limited by synchronization overhead. Asynchronous federated learning (AFL) alleviates this issue by allowing clients to communicate independently, thereby improving wall-clock efficiency in large-scale, hardware-heterogeneous environments. However, asynchrony introduces updates computed on outdated global models (staleness) that can destabilize optimization and hinder convergence. We propose FedRevive, an AFL framework that revives stale updates through data-free knowledge distillation (DFKD). FedRevive integrates parameter-space aggregation with a server-side DFKD process that transfers knowledge from stale client updates to the current global model without access to data. A meta-learned generator synthesizes pseudo-samples for multi-teacher distillation. A hybrid aggregation scheme combining raw with DFKD updates effectively mitigates staleness while retaining AFL scalability. Experiments on vision and text benchmarks show that FedRevive achieves faster training by up to 29.7\% and higher final accuracy by up to 11.4\% percentage points than baselines.

Federated Multi-View Learning (FedMVL) enables multiple clients with heterogeneous data views to collaboratively train global models without sharing private data. Existing FedMVL algorithms often overlook the reliability of local predictions, as both uncertainty quantification and calibration remain challenging in privacy-preserving federated settings. Heterogeneous sensing devices yield varying view qualities, but view-isolated training further exacerbates inherent bias of local model, inducing unreliable outputs. Without proper calibration, these artifacts cause spurious cross-view conflicts, ultimately undermining the accuracy and reliability of the aggregated global model. To address these issues, we propose a Reliable Federated Multi-View Learning with evidence calibration (FedRMVL-CAL), a FedMVL framework with adaptive evidence fusion. The local calibration scheme harmonizes uncertainty across views, while the fusion mechanism adaptively reconciles conflicting opinions, enhancing robustness in global inference. Theoretical analysis and extensive experiments demonstrate that FedRMVL-CAL achieves superior accuracy and reliability compared to existing approaches, ensuring trustworthy global predictions across heterogeneous views.

Federated Incremental Learning (FIL) aims to learn sequential tasks across distributed clients under strict privacy constraints while mitigating catastrophic forgetting. However, existing methods often rely on storing, transmitting, or generating previous data to retrain the model, which not only introduces potential privacy risks but also incurs additional memory, communication, and computational costs, thereby limiting their deployment on resource-constrained edge devices. To tackle this challenge, we propose \textit{SPR-FIL}, a novel rehearsal-free and lightweight \underline{S}tatistical \underline{P}rototype \underline{R}egularization framework for \underline{F}ederated \underline{I}ncremental \underline{L}earning that leverages Gaussian statistics and preserves previous knowledge through aligned prototypes and subspace-constrained encoder updates. More specifically, the server aggregates client-side statistics into global prototypes and dynamically aligns historical prototypes with the evolving feature space through anchor-based moment matching. Locally, clients utilize these aligned prototypes to induce virtual posteriors which serve as soft supervision for distillation while encoder updates are further regularized by projecting gradients onto the orthogonal complement of covariance-induced historical knowledge subspaces. Extensive experiments demonstrate that SPR-FIL is model-agnostic, communication-efficient, and consistently outperforms state-of-the-art methods across various FIL settings, achieving improvements of up to 9.73\%.

Random Forests (RF) are among the most powerful and widely used predictive models for centralized tabular data, yet few methods exist to adapt them to the federated learning setting. Unlike most federated learning approaches, the piecewise-constant nature of RF prevents exact gradient-based optimization. As a result, existing federated RF implementations rely on unprincipled heuristics: by aggregating decision trees trained independently on clients, they fail to optimize the centralized decision-tree impurity criterion, which constitutes the ground-truth in federated settings, even under simple covariate shifts. We propose FedForest, a new federated RF algorithm for horizontally partitioned data that naturally accommodates diverse forms of client data heterogeneity, from covariate shift to more complex concept shift mechanisms. We prove that our splitting procedure, based on aggregated client statistics, closely approximates the split selected by a centralized algorithm. Moreover, FedForest allows splits on client indicators when beneficial, enabling a non-parametric form of personalization that is absent from prior federated random forest methods. Empirically, we demonstrate that the resulting federated forests closely match centralized performance across heterogeneous benchmarks while remaining communication-efficient.

Auction-based Federated Learning (AFL) provides a principled framework for incentivizing data owners and allocating decentralized training resources to data consumers. However, practical AFL systems face a coupled valuation and allocation challenge: data quality is uncertain before training, post-training feedback can be strategically distorted, and heterogeneous deadlines render late model updates ineffective for learning. We propose MainFL, an incentive-compatible intertemporal data valuation mechanism for robust auction-based federated learning. MainFL integrates three components: (i) a bounded mutual-information peer prediction rule that elicits truthful pre-training quality reports without requiring ground-truth labels; (ii) an intertemporal reliability calibration scheme that updates future valuations using post-session validation signals; and (iii) a deadline-induced layered clinching mechanism that allocates quality-weighted data while enforcing budget and feasibility constraints.We theoretically establish Bayes–Nash truthfulness for data owners, allocation feasibility and budget safety, and truthful residual-demand equilibrium for data consumers under standard clinching-auction regularity conditions. Empirical results on widely used federated learning benchmarks show that MainFL improves social welfare by up to 12.1% and model accuracy by up to 7.4% compared to state-of-the-art AFL baselines, while substantially reducing straggler-induced task timeouts.

We consider the problem of learning to adapt a foundation model in a federated setting, particularly the most realistic and general setting: 1) When the local data sets are sampled from different distributions but we want to learn a globally adapted model, 2) Where local agents enter and leave the federation asynchronously at each time tick which is beyond the control of the learning algorithm, and 3) Where the goal is continuous adaptation so that after each time tick, the learned adapter generalizes accurately for all participants that have been seen during training. We propose a simple idea called federated *library-based adaptation* (*LEAN*) for this setting. In library-based adaptation, the system maintains a pool, or "library" of rank-1 "basis pairs". Groups of these pairs are distributed to local sites to be trained in LoRA fashion, and then separated and *shuffled* at communication. Library-based adaptation is designed to avoid problems with more conventional methods, such as those based on averaging. In particular, we demonstrate *LEAN* outperforms traditional averaging baselines in both communication and computation cost efficiency across a broad range of important settings, including heavy data skewness and high asynchrony.

Federated Continual Learning (FCL) requires distributed clients to acquire new tasks while retaining previously learned knowledge. In this paper, we revisit the heterogeneity challenge in FCL, which is composed of two distinct aspects: spatial static heterogeneity, e.g., initial data distribution and model architecture differences, and temporal dynamic heterogeneity, e.g., task shifts over time. While existing methods acknowledge the presence of heterogeneity, they typically treat it in a unified manner by employing a single adaptive strategy without explicitly disentangling their impacts. In contrast, we propose a heterogeneity-aware distillation framework for FCL called Ha-FCL, which is designed to handle spatial and temporal heterogeneity through tailored distillation strategies. Specifically, Ha-FCL combines width-aware federated training with server-side public-anchor distillation, integrating multi-layer feature distillation for architecture-aware alignment with historical-logit regularization and representation co-distillation. This forms a coordinated dual-dimensional distillation scheme that balances adaptation to new tasks with retention of prior knowledge. This enables more targeted knowledge transfer and leads to improved model performance under complex FCL scenarios. We evaluate our method in various heterogeneous settings. Compared with state-of-the-art baselines, Ha-FCL consistently improves test accuracy and shows better robustness under challenging heterogeneous FCL settings.

Federated continual learning (FCL) evaluates how distributed clients learn from changing data streams while retaining previously learned knowledge. Existing evaluations are difficult to compare because they often change datasets, task splits, client data splits, task orders, backbones, memory assumptions, and reporting rules simultaneously. We introduce \textbf{HERO}, a heterogeneity-aware benchmark library for FCL. HERO builds benchmark streams by separating three choices that are often coupled, namely the task split, the client data split, and the client task sequence. In HERO-Core, the main comparable benchmark, $\alpha$ controls client data skew and $\rho$ controls task-order mismatch. We evaluate representative FCL methods on CIFAR-100 and TinyImageNet using final average accuracy, average forgetting, and bottom-10\% client accuracy. We also include a graph-based Domain-IL portability case study on OGB-MolPCBA, where scaffold-domain granularity changes the input distribution while the prediction task remains fixed. Our results show that method behavior changes across easy and heterogeneous settings, that average accuracy can hide weak bottom-client performance, that task-order mismatch favors different strategies from synchronized evaluation, and that the same HERO interface can expose domain-shift difficulty beyond image-based FCIL. HERO releases benchmark streams, configurations, method implementations, and reporting scripts to support reproducible and setting-aware FCL evaluation.

Knowledge distillation provides shared semantic supervision for local training in federated learning, reducing the tendency of client models to overfit their own data distributions. Existing methods construct a global knowledge repository from client-uploaded local information to establish consensus across clients. However, global knowledge that is fundamentally derived from client data inevitably contains local biases under heterogeneous settings. To address this issue, this study proposes a geometry-regularized cross-silo consensus enhancement method, termed RISE, which improves collapse resistance in federated learning by preserving pre-trained geometric consensus priors during local adaptation. Specifically, RISE introduces two complementary geometry regularizers. The Biased Low-dimensional Subspace Correction module preserves the spectral energy profile of pre-trained representations by constraining the energy distribution of adapted features within the principal representation subspace. Instead of enforcing direction-wise alignment, it discourages excessive energy concentration along a few dominant components, thereby mitigating biased low-dimensional feature collapse. The Manifold Relation Graph Alignment module regularizes the relational manifold geometry by constructing a transferable inter-class topology from pre-trained global class prototypes and aligning client-side class structures with this global relation, thereby mitigating class representation drift and stabilizing global aggregation. Extensive experiments on eight benchmark datasets across two tasks demonstrate that RISE improves cross-client consensus consistency and enhances the generalization ability of the global model, outperforming eight state-of-the-art methods.

Data heterogeneity and multiple local update steps in federated learning can increase client drift and make global parameter aggregation less stable. Existing Sharpness Aware Minimization (SAM) based federated learning methods usually try to ease this problem by flattening local neighborhoods around client models. However, they do not explicitly optimize on the geometry region where the global parameters are actually aggregated. As a result, the aggregated global model often cannot fully benefit from local flattening. To address this problem, we propose a federated restricted SAM method called FedRSAM, which improves the stability of federated optimization by flattening the parameter aggregation region. First, we build a cone-shaped geometry region using the global update direction from the previous round and the variance of client parameter aggregation, in order to predict where the aggregated parameters may fall in the next round. Then, we restrict the search of SAM perturbation directions to the cone-manifold and only flatten the loss surface inside this region. Extensive experiments on multiple benchmarks and on both vision and natural language backbones verify the superiority of our method in terms of convergence stability and performance under heterogeneous settings.

Federating a LGN is challenging because each gate is parameterised by a distribution over 16 Boolean operations, so averaging parameters across clients does not correspond to averaging the underlying functions. Under non-IID data, such parameter-space merging collapses to near-chance accuracy. Boolean Feature Selection (BFS) avoids this issue by selecting discrete Boolean functions rather than averaging parameters. We propose Federated Boolean Feature Selection (FBFS), which extends BFS to LGNs by treating hardened last-layer gates as candidate Boolean features and aggregating per-class, per-gate sufficient statistics across clients. Clients share only firing aggregates computed on a held-out split, allowing the server to reconstruct discriminative scores, defined as the gap between a gate's mean firing for a class and for the remaining classes, without accessing raw data. This procedure is equivalent to centralized BFS on the union of client data. We further introduce a diversity-regularized variant of FBFS that encourages complementary feature selection across gates. Empirically, FBFS is the only data-free method we evaluate whose performance remains stable under extreme non-IID settings, outperforming parameter-space merging and other baselines by large margins. It maintains high accuracy across a wide range of heterogeneity levels and scales to large architectures. In addition, FBFS enables efficient deployment: the resulting models compile to hardware-efficient logic representations with substantially fewer post-synthesis cells than ensemble-based alternatives while remaining bit-exact with their software counterparts.

Federated Learning (FL) algorithms implicitly assume that clients passively comply with server-side orchestration by sharing local model updates upon server request. However, this overlooks an important aspect in real-world cross-silo environments: clients are often rational agents who may prioritize their utilities such as local model performance over that of the global model. In settings with significant statistical heterogeneity, rational clients may opt out of the federation if the perceived benefits of collaboration fail to meet their local utility thresholds. Such attrition degrades the global model performance and can lead to the collapse of the federated training process. In this work, we introduce FedUCA: Federated Learning by Utility-Constrained Stochastic Aggregation for Improving Rational Participation, a framework that formalizes the server's role as an optimizer seeking to maximize global model performance by sustaining client participation. We substantiate our framework through extensive experiments on standard datasets demonstrating that by prioritizing participation feasibility, FedUCA achieves significantly higher client retention and, consequently, a superior global model performance.

Federated Graph Learning (FGL) aims to collaboratively train Graph Neural Networks (GNNs) across distributed subgraphs with a primary challenge of missing cross-client connections. Existing approaches predominantly rely on a retrieval-based paradigm, which reconstructs missing context by accessing external information across clients. However, such cross-client transmissions inevitably expand the attack surface for privacy inference and incur significant communication overhead. In this work, we propose \textbf{FedLMC}, a novel framework that eliminates the necessity of any node information sharing and prior global adjacency knowledge. FedLMC exploits a dual-stage message compensation mechanism to generate informative and expressive node representations. For the semantic information loss of external neighbors, we propose Local Semantic Compensation, which exploits semantic expansion via semantic surrogates to compensate for missing semantic context. For the structural information loss induced by semantic expansion, we propose Community Structural Compensation, which exploits structural anchoring via community anchors to compensate for underlying structural context. Extensive experiments on twelve datasets (both homophilic and heterophilic) demonstrate that FedLMC establishes a new state-of-the-art performance with superior generalization and robustness to highly fragmented graph data.

Federated Continual Learning (FCL) suffers from spatial-temporal catastrophic forgetting caused by sequential task learning on individual clients and the aggregation of heterogeneous client knowledge. However, most existing methods either overemphasize preserving old knowledge, which restricts model plasticity for new tasks, or rely on training complex generative models for replay, incurring substantial computational overhead. In the human immune system, immune memory is preserved implicitly and can be reactivated through vaccination even after long periods of dormancy. Motivated by this, we propose ***FedVaccine***, a novel framework that explicitly permits forgetting to preserve high plasticity for new tasks, while efficiently recovering past knowledge through re-learning a compact vaccine set. Specifically, the vaccine set for each task is synthesized by compress task gradient information under the guidance of the fine-tuned local model, capturing representative data characteristics and task-specific discriminative patterns. Two novel components are also introduced: ***Priming & Boosting***, which encodes task-specific knowledge in a compact spectral form, priming the model with essential information and enabling efficient memory boosting via vaccine set replay after forgetting; and ***Vaccination Aggregation***, which aggregates vaccine sets from all clients to train a globally generalized model without accessing any raw private data. Extensive experiments on three benchmarks demonstrate that FedVaccine achieves competitive performance, enables rapid knowledge recovery after forgetting, and incurs no additional overhead from training generative models.