arXiv Machine Learning July 16, 2026 By Eunjeong Jeong, Nikolaos Pappas

Computation-aware Energy-harvesting Federated Learning with Pipelined Cyclic Scheduling

Read the original on arXiv Machine Learning →

arXiv:2511. 11949v2 Announce Type: replace Abstract: Federated learning (FL) is a powerful paradigm for distributed learning, but increasing model complexity leads to significant energy consumption from client-side computations for local training.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

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In recent years, the Internet of Things (IoT) paradigm has been shifting toward batteryless, energy-harvesting architectures. Sustaining reliable operation in these systems requires intelligent management of highly volatile stored energy.

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Cluster-Aware Over-the-Air Federated Learning with Energy-Harvesting Devices: From Global Training to Model Personalization

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