Federated Learning within Global Energy Budget over Heterogeneous Edge Accelerators

2025/06/12 by Banerjee, Roopkatha, Chandrashekar, Tejus, Eswar, Ananth +1
#Distributed #FOS: Computer and information sciences #Parallel #and Cluster Computing (cs.DC)

paper · doi:10.48550/arxiv.2506.10413

Abstract

Federated Learning (FL) enables collaborative model training across distributed clients while preserving data privacy. However, optimizing both energy efficiency and model accuracy remains a challenge, given device and data heterogeneity. Further, sustainable AI through a global energy budget for FL has not been explored. We propose a novel optimization problem for client selection in FL that maximizes the model accuracy within an overall energy limit and reduces training time. We solve this with a unique bi-level ILP formulation that leverages approximate Shapley values and energy-time prediction models to efficiently solve this. Our FedJoule framework achieves superior training accuracies compared to SOTA and simple baselines for diverse energy budgets, non-IID distributions, and realistic experiment configurations, performing 15% and 48% better on accuracy and time, respectively. The results highlight the effectiveness of our method in achieving a viable trade-off between energy usage and performance in FL environments.

Citations

• HeteroSwitch: Characterizing and Taming System-Induced Data Heterogeneity in Federated Learning
• GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated Learning
• Towards Efficient Data Valuation Based on the Shapley Value
• Federated Optimization in Heterogeneous Networks
• A Unified Approach to Interpreting Model Predictions

Related

• Towards Benchmarking Power-Performance Characteristics of Federated Learning Clients
• Towards Energy-Aware Federated Learning via MARL: A Dual-Selection Approach for Model and Client
• Learn More by Using Less: Distributed Learning with Energy-Constrained Devices
• Fine-Grained Data Selection for Improved Energy Efficiency of Federated Edge Learning
• AutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated Learning