AI RESEARCH
ERIS: Enhancing Privacy and Scalability in Federated Learning via Federated Shard Aggregation
arXiv CS.LG
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ArXi:2602.08617v2 Announce Type: replace Scaling Federated Learning (FL) to billion-parameter models forces a challenging trade-off between privacy, scalability, and model utility. Existing solutions often tackle these challenges in isolation, sacrificing accuracy, relying on costly cryptographic tools, or