Family-FL uses family-level aggregation in a three-tier setup with a sub-5KB quantized CNN-LSTM to cut communication by 76.7% versus FedAvg while reaching 91.9% accuracy on MIT-BIH arrhythmia data.
InProceedings of Machine Learning and Systems (MLSys)(mlsys.org),2: 429–
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Towards Family-Grouped Hierarchical Federated Learning on Sub-5KB Models: A Feasibility Study of Privacy-Preserving ECG Monitoring for Ultra-Resource-Constrained Wearables
Family-FL uses family-level aggregation in a three-tier setup with a sub-5KB quantized CNN-LSTM to cut communication by 76.7% versus FedAvg while reaching 91.9% accuracy on MIT-BIH arrhythmia data.