IFedAvg: Interpretable Data-Interoperability for Federated Learning
arXiv:2107.06580
Abstract
Recently, the ever-growing demand for privacy-oriented machine learning has motivated researchers to develop federated and decentralized learning techniques, allowing individual clients to train models collaboratively without disclosing their private datasets. However, widespread adoption has been limited in domains relying on high levels of user trust, where assessment of data compatibility is essential. In this work, we define and address low interoperability induced by underlying client data inconsistencies in federated learning for tabular data. The proposed method, iFedAvg, builds on federated averaging adding local element-wise affine layers to allow for a personalized and granular understanding of the collaborative learning process. Thus, enabling the detection of outlier datasets in the federation and also learning the compensation for local data distribution shifts without sharing any original data. We evaluate iFedAvg using several public benchmarks and a previously unstudied collection of real-world datasets from the 2014 - 2016 West African Ebola epidemic, jointly forming the largest such dataset in the world. In all evaluations, iFedAvg achieves competitive average performance with negligible overhead. It additionally shows substantial improvement on outlier clients, highlighting increased robustness to individual dataset shifts. Most importantly, our method provides valuable client-specific insights at a fine-grained level to guide interoperable federated learning.
References in corpus (14)
- PyTorch: An Imperative Style, High-Performance Deep Learning Library
- Communication-Efficient Learning of Deep Networks from Decentralized Data
- Federated Learning with Non-IID Data
- Model-Agnostic Interpretability of Machine Learning
- Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification
- Federated Learning with Personalization Layers
- Federated Optimization in Heterogeneous Networks
- Improving Federated Learning Personalization via Model Agnostic Meta Learning
- Personalized Federated Learning: A Meta-Learning Approach
- Adaptive Personalized Federated Learning
- Adaptive Federated Optimization
- Variational Federated Multi-Task Learning
- Salvaging Federated Learning by Local Adaptation
- Interpret Federated Learning with Shapley Values