A federated system combining autoencoders, FedAvg, Renyi DP-SGD, and INT8 quantization matches centralized AUROC performance (0.782 for ConvAE) on PTB-XL while halving model size and cutting edge latency by up to 44% with independent privacy and quantization penalties.
Elmir et al.Federated Learning with Gramian Angular Fields for Privacy-Preserving ECG Classification on Heterogeneous IoT Devices
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Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices
A federated system combining autoencoders, FedAvg, Renyi DP-SGD, and INT8 quantization matches centralized AUROC performance (0.782 for ConvAE) on PTB-XL while halving model size and cutting edge latency by up to 44% with independent privacy and quantization penalties.