Lightweight Federated Learning over Wireless Edge Networks
This work addresses communication overhead and privacy issues for smart devices in wireless networks, representing an incremental improvement in federated learning deployment.
The paper tackles the challenge of deploying federated learning in wireless edge networks by proposing a lightweight framework that integrates transmission power control, model pruning, and gradient quantization, achieving superior performance over state-of-the-art schemes in experiments on real-world datasets.
With the exponential growth of smart devices connected to wireless networks, data production is increasing rapidly, requiring machine learning (ML) techniques to unlock its value. However, the centralized ML paradigm raises concerns over communication overhead and privacy. Federated learning (FL) offers an alternative at the network edge, but practical deployment in wireless networks remains challenging. This paper proposes a lightweight FL (LTFL) framework integrating wireless transmission power control, model pruning, and gradient quantization. We derive a closed-form expression of the FL convergence gap, considering transmission error, model pruning error, and gradient quantization error. Based on these insights, we formulate an optimization problem to minimize the convergence gap while meeting delay and energy constraints. To solve the non-convex problem efficiently, we derive closed-form solutions for the optimal model pruning ratio and gradient quantization level, and employ Bayesian optimization for transmission power control. Extensive experiments on real-world datasets show that LTFL outperforms state-of-the-art schemes.