Fed-BAC uses contextual bandits and Thompson Sampling with additive clustering to deliver up to 35.5 percentage point accuracy gains and 1.5-4.8x faster convergence in hierarchical federated learning on non-IID data.
Personalizing federated learning for hierarchical edge networks with non-IID data
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Fed-BAC: Federated Bandit-Guided Additive Clustering in Hierarchical Federated Learning
Fed-BAC uses contextual bandits and Thompson Sampling with additive clustering to deliver up to 35.5 percentage point accuracy gains and 1.5-4.8x faster convergence in hierarchical federated learning on non-IID data.