A Theoretical Framework for Energy-Aware Gradient Pruning in Federated Learning

• URL: http://arxiv.org/abs/2603.22465v1
• Date: Mon, 23 Mar 2026 18:31:07 GMT
• Title: A Theoretical Framework for Energy-Aware Gradient Pruning in Federated Learning
• Abstract summary: Federated Learning (FL) is constrained by the communication and energy limitations of decentralized edge devices.<n>We propose Cost-Weighted Magnitude Pruning (CWMP), a selection rule that prioritizes parameter updates based on their magnitude relative to their physical cost.<n> Numerical results on a non-IIDAR-10 benchmark show that CWMP consistently establishes a superior performance-energy frontier compared to the Top-K baseline.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated Learning (FL) is constrained by the communication and energy limitations of decentralized edge devices. While gradient sparsification via Top-K magnitude pruning effectively reduces the communication payload, it remains inherently energy-agnostic. It assumes all parameter updates incur identical downstream transmission and memory-update costs, ignoring hardware realities. We formalize the pruning process as an energy-constrained projection problem that accounts for the hardware-level disparities between memory-intensive and compute-efficient operations during the post-backpropagation phase. We propose Cost-Weighted Magnitude Pruning (CWMP), a selection rule that prioritizes parameter updates based on their magnitude relative to their physical cost. We demonstrate that CWMP is the optimal greedy solution to this constrained projection and provide a probabilistic analysis of its global energy efficiency. Numerical results on a non-IID CIFAR-10 benchmark show that CWMP consistently establishes a superior performance-energy Pareto frontier compared to the Top-K baseline.

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