arxiv
PublishedJune 11, 2026 at 4:00 AM
Federated continual learning: A comprehensive survey on lifelong and privacy-preserving learning over distributed and non-stationary data
Publisher summary· verbatim
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), cybers
Stay posted· Newsletter
A 5-min weekly brief — top movers, price watch, story of the week.
Discussion
No replies yet. Be first.
Related coverage
More from ARXIV
arxivGeneralizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Solutions38marxivMARIC: Multi-Agent Reasoning for Image Classification38marxivThe Impossibility of Eliciting Latent Knowledge38marxivA Five-Plane Reference Architecture for Runtime Governance of Production AI Agents38mThe Bubble Brief
WEEKLYRead AI insights every Tuesday — top movers, new releases, story of the week.
Originally published on arxiv ↗