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influence-based-weighting

topic1 events
papersSEP 10 04:00 UTC

Researchers Propose Influence-Based Weighting for Personalized Federated Learning

A revised arXiv preprint introduces a personalized federated learning method that weights each client's parameter updates according to its influence, rather than relying on fixed aggregation weights. The approach is designed to let devices with different data distributions and preferences train collaboratively while keeping their data private. The updated version appears across arXiv's cs.AI and cs.LG listings.

federated-learning/privacy/personalization/machine-learning