FedPDA: Collaborative Learning for Reducing Online-Adaptation Frequency of Neural Receivers

Abstract

Wireless neural receivers provide a promising alternative to conventional receivers. To perform well in different channel environments, online adaption is required. However, during this process, performance remains low. Thus, an approach called federated collaborative learning with pruned-data aggregation (FedPDA) is developed to reduce online-adaptation frequency. The basic idea is that, upon online adaptation, mobile terminals further update their neural receivers collaboratively via federated learning. To reduce memory consumption, neural receivers follow a main-side network architecture where only the side network needs retraining during collaborative learning. To avoid catastrophic forgetting during continual learning, local data on terminals are pruned, with only a small percent sent to the base station. With such data, the base station also trains a neural receiver before conducting model aggregation. FedPDA is distinct with several features: 1) small memory footprint and no storage burden on terminals; 2) no catastrophic forgetting issue; 3) low communication cost. Performance results show that FedPDA reduces online adaptation by more than 90% and memory footprint by 70%. It achieves comparable performance as centralized schemes, but reducing communication cost by 78%. Compared to vanilla federated learning, FedPDA resolves the catastrophic forgetting issue without storage burden, and also reduces the communication cost by 50%.