AI RESEARCH
CRAFT: Conflict-Resolved Aggregation for Federated Training
arXiv CS.LG
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ArXi:2605.21317v1 Announce Type: new The aggregation of conflicting client updates remains a fundamental bottleneck in federated learning (FL) under heterogeneous data distributions. Naive averaging can produce a global update that improves the global objective while conflicting with specific clients, causing degradation for those clients. In this work, we propose CRAFT (Conflict-Resolved Aggregation for Federated