arXiv Machine Learning July 9, 2026 By Dhruv Garg, Neha Lakhani, Debopam Sanyal, Myungjin Lee, Alexey Tumanov, Ada Gavrilovska

Robust Federated Learning Under Real-World Client Churn

Read the original on arXiv Machine Learning →

arXiv:2607. 06979v1 Announce Type: new Abstract: Federated Learning (FL) enables training shared models on private, on-device data, but production deployments remain constrained to slow, multi-day refresh cycles due to the complexity of coordinating massive client populations.

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

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