OmniISR unifies centralized, federated, and hybrid learning by injecting mutual-information supervision and negative-entropy regularization at multiple hidden layers, with supporting convergence and drift bounds.
Tighter regret analysis and optimization of online federated learning,
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OmniISR: A Unified Framework for Centralized and Federated Learning via Intermediate Supervision and Regularization
OmniISR unifies centralized, federated, and hybrid learning by injecting mutual-information supervision and negative-entropy regularization at multiple hidden layers, with supporting convergence and drift bounds.