Kavli Affiliate: Yi Zhou
| First 5 Authors: Yi Zhou, Naman Goel, , ,
| Summary:
Federated Learning (FL) allows collaborative model training among distributed
parties without pooling local datasets at a central server. However, the
distributed nature of FL poses challenges in training fair federated learning
models. The existing techniques are often limited in offering fairness
flexibility to clients and performance. We formally define and empirically
analyze a simple and intuitive post-processing-based framework to improve group
fairness in FL systems. This framework can be divided into two stages: a
standard FL training stage followed by a completely decentralized local
debiasing stage. In the first stage, a global model is trained without fairness
constraints using a standard federated learning algorithm (e.g. FedAvg). In the
second stage, each client applies fairness post-processing on the global model
using their respective local dataset. This allows for customized fairness
improvements based on clients’ desired and context-guided fairness
requirements. We demonstrate two well-established post-processing techniques in
this framework: model output post-processing and final layer fine-tuning. We
evaluate the framework against three common baselines on four different
datasets, including tabular, signal, and image data, each with varying levels
of data heterogeneity across clients. Our work shows that this framework not
only simplifies fairness implementation in FL but also provides significant
fairness improvements with minimal accuracy loss or even accuracy gain, across
data modalities and machine learning methods, being especially effective in
more heterogeneous settings.
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