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Federated learning enables data sharing in healthcare contexts where it mightotherwise be difficult due to data-use-ordinances or security and communicationconstraints. Distributed and shared data models allow models to becomegeneralizable and learn from heterogeneous clients. While addressing datasecurity, privacy, and vulnerability considerations, data itself is not sharedacross nodes in a given learning network. On the other hand, FL models oftenstruggle with variable client data distributions and operate on an assumptionof independent and identically distributed data. As the field has grown, thenotion of fairness-aware federated learning mechanisms has also been introducedand is of distinct significance to the healthcare domain where many sensitivegroups and protected classes exist. In this paper, we create a benchmarkmethodology for FAFL mechanisms under various heterogeneous conditions ondatasets in the healthcare domain typically outside the scope of currentfederated learning benchmarks, such as medical imaging and waveform dataformats. Our results indicate considerable variation in how various FAFLschemes respond to high levels of data heterogeneity. Additionally, doing sounder privacy-preserving conditions can create significant increases in networkcommunication cost and latency compared to the typical federated learningscheme.

Towards Fair and Privacy Preserving Federated Learning for the Healthcare Domain