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arXiv Machine Learning By Kaijie Chen, Alex Johnson, Maria Garcia, Wei Zhang, Daniel Kim

FedCausal-Dyn: A Causal-Dynamic Paradigm for Federated Learning under Dynamic Feature Drift

Read the original on arXiv Machine Learning →

arXiv:2607. 09695v1 Announce Type: new Abstract: This paper addresses the challenging problem of dynamic feature drift in federated learning, where data distributions evolve across clients and over time -- a common scenario in real-world applications like financial technology.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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