Fbsubnet+l (iOS WORKING)

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Rapid view delivery, video shares, and follower expansion loops.

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Federated Learning (FL) has emerged as a promising paradigm for distributed machine learning, enabling multiple clients to collaboratively train a model while preserving data privacy. However, FL faces significant challenges, including non-IID data distributions, communication overhead, and model convergence issues. In this paper, we propose FBSubnet+L, a novel approach that integrates subnetwork training and local learning to address these challenges. Our approach leverages the benefits of subnetworks to reduce communication overhead and improve model convergence, while incorporating local learning to adapt to client-specific data distributions. We provide a detailed analysis of FBSubnet+L, including its architecture, algorithm, and theoretical guarantees. Our experimental results demonstrate the effectiveness of FBSubnet+L in outperforming state-of-the-art FL methods.