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Federated continual learning: A comprehensive survey on lifelong and privacy-preserving learning over distributed and non-stationary data

Masoume Gholizade, Fabrizio Ruffini, Pietro Ducange, Francesco Marcelloni
Thursday at 04:00
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arXiv:2606.11272v1 Announce Type: cross Abstract: Federated Learning (FL) enables collaborative and privacy-preserving model training across distributed clients, but most existing FL systems implicitly assume data stationarity. In real-world settings-such as healthcare, industrial IoT (IIOT), cybersecurity, and smart cities-data streams are...

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