Cluster-Aware Over-the-Air Federated Learning with Energy-Harvesting Devices: From Global Training to Model Personalization
Federated learning (FL) enables distributed optimization and learning across decentralized edge devices while preserving data privacy, but its performance is fundamentally constrained by heterogeneous data distributions, limited communication resources, and energy availability. In practical wireless networks, mobile devices (MDs) often exhibit diverse data and learning objectives, naturally forming clusters of users with jointly trainable models. When devices rely on energy harvesting (EH), stoc
Record details
Published: 2 August 2026
Source: arXiv fairness query
Category: Research
Topics: Privacy · Environment
Retrieved: 4 August 2026
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ethics.ai (2 August 2026), “Cluster-Aware Over-the-Air Federated Learning with Energy-Harvesting Devices: From Global Training to Model Personalization,” evidence record 16130, https://ethics.ai/record/16130 (originally published by arXiv fairness query).
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