LoLaFL: Low-Latency Federated Learning via Forward-only Propagation
Jierui Zhang, Jianhao Huang, Kaibin Huang · 2025
Federated learning (FL) has emerged as a widely adopted paradigm for enabling edge learning with distributed data while ensuring data privacy. However, the traditional FL with deep neural networks trained via backpropagation can hardly meet the low-latency learning requirements in the sixth generation (6G) mobile networks. This challenge mainly arises from the high-dimensional model parameters to be transmitted and the numerous rounds of communication required for convergence. To address this issue, we adopt the state-of-the-art principle of maximal coding rate reduction to learn linear discriminative features and extend the resultant white-box neural network into FL, yielding the novel framework of Low-Latency Federated Learning (LoLaFL) via forward-only propagation. LoLaFL enables layer-wise transmissions and aggregation with significantly fewer communication rounds, thereby considerably reducing latency. Additionally, we propose a nonlinear aggregation scheme for LoLaFL, which is based on the proof that the optimal NN parameter aggregation in LoLaFL should be harmonic-mean-like. Experiments demonstrate that LoLaFL can achieve over 91% reduction in latency in comparison with traditional FL, while maintaining comparable accuracy.