OmniISR: A Unified Framework for Centralized and Federated Learning via Intermediate Supervision and Regularization

In three linesOmniISR proposes a unified framework for centralized and federated learning via intermediate supervision and regularization. The framework uses mutual information to align internal covariate shifts and negative entropy to regularize overconfident predictions. O(1/sqrt(T)) convergence guaranteed theoretically; CL-FL gap reduced by 22.60% in experiments.

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