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HFLB is a benchmark for heterogeneous federated learning containing between 100,000 and 1,000,000 records, developed by SNUMPR for the FedMosaic (ICLR 2026) study. It modifies constituent datasets like GQA and Abstract VQA into distinct subtasks to support task incremental learning research.
Users must consult the FedMosaic paper for the specific configuration and subtask mapping used in this version of HFLB.