Communication-Efficient Federated Learning with Adaptive Parameter Freezing

Federated learning allows edge devices to collaboratively train a global model by synchronizing their local updates without sharing private data. Yet, with limited network bandwidth at the edge, communication often becomes a severe bottleneck. In this paper, we find that it is unnecessary to always synchronize the full model in the entire training process, because many parameters gradually stabilize prior to the ultimate model convergence, and can thus be excluded from being synchronized at an early stage. This allows us to reduce the communication overhead without compromising the model accuracy. However, challenges are that the local parameters excluded from global synchronization may diverge on different clients, and meanwhile some parameters may stabilize only temporally. To address these challenges, we propose a novel scheme called Adaptive Parameter Freezing (APF), which fixes (freezes) the non-synchronized stable parameters in intermittent periods. Specifically, the freezing periods are tentatively adjusted in an additively-increase and multiplicatively-decrease manner, depending on if the previously-frozen parameters remain stable in subsequent iterations. We implemented APF as a Python module in PyTorch. Our extensive array of experimental results show that APF can reduce data transfer by over 60%.