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Non-IID data traits can drastically influence Federated Learning convergence, with measurable impacts that practitioners can leverage for more stable training outcomes.
Behavioral identifiability of language model adapters reveals a consistent trade-off with distributional similarity, challenging assumptions about community conditioning in LLMs.
