Joint Optimization of Energy Consumption and Completion Time in Federated Learning

TL;DR

This paper proposes a joint optimization framework for federated learning that balances energy consumption and completion time by optimizing resource allocation, demonstrating improved performance over existing methods.

Contribution

It introduces a novel optimization model and algorithm for balancing energy and latency in federated learning, with convergence analysis and superior numerical results.

Findings

Optimized resource allocation reduces energy and time costs.

The proposed algorithm outperforms existing methods.

Flexible weighting adapts to different application demands.

Abstract

Federated Learning (FL) is an intriguing distributed machine learning approach due to its privacy-preserving characteristics. To balance the trade-off between energy and execution latency, and thus accommodate different demands and application scenarios, we formulate an optimization problem to minimize a weighted sum of total energy consumption and completion time through two weight parameters. The optimization variables include bandwidth, transmission power and CPU frequency of each device in the FL system, where all devices are linked to a base station and train a global model collaboratively. Through decomposing the non-convex optimization problem into two subproblems, we devise a resource allocation algorithm to determine the bandwidth allocation, transmission power, and CPU frequency for each participating device. We further present the convergence analysis and computational complexity of the proposed algorithm. Numerical results show that our proposed algorithm not only has better performance at different weight parameters (i.e., different demands) but also outperforms the state of the art.