AI RESEARCH
SCOPE: Semantic Coreset with Orthogonal Projection Embeddings for Federated learning
arXiv CS.LG
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ArXi:2603.12976v1 Announce Type: new Scientific discovery increasingly requires learning on federated datasets, fed by streams from high-resolution instruments, that have extreme class imbalance. Current ML approaches either require impractical data aggregation or fail due to class imbalance. Existing coreset selection methods rely on local heuristics, making them unaware of the global data landscape and prone to sub-optimal and non-representative pruning. To overcome these challenges, we