Towards Model Agnostic Federated Learning Using Knowledge Distillation

Abstract

Is it possible to design an universal API for federated learning using which an ad-hoc group of data-holders (agents) collaborate with each other and perform federated learning? Such an API would necessarily need to be model-agnostic i.e. make no assumption about the model architecture being used by the agents, and also cannot rely on having representative public data at hand. Knowledge distillation (KD) is the obvious tool of choice to design such protocols. However, surprisingly, we show that most natural KD-based federated learning protocols have poor performance. To investigate this, we propose a new theoretical framework, Federated Kernel ridge regression, which can capture both model heterogeneity as well as data heterogeneity. Our analysis shows that the degradation is largely due to a fundamental limitation of knowledge distillation under data heterogeneity. We further validate our framework by analyzing and designing new protocols based on KD. Their performance on real world experiments using neural networks, though still unsatisfactory, closely matches our theoretical predictions.

Published at ICLR 2022

References in corpus (11)

• Distilling the Knowledge in a Neural Network
• Knowledge Distillation: A Survey
• Ensemble Distillation for Robust Model Fusion in Federated Learning
• A Field Guide to Federated Optimization
• Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning
• Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning
• Why distillation helps: a statistical perspective
• Communication-Efficient Federated Distillation
• Practical One-Shot Federated Learning for Cross-Silo Setting
• Optimal Model Averaging: Towards Personalized Collaborative Learning
• A Closer Look at Codistillation for Distributed Training