Ensemble Distillation for Robust Model Fusion in Federated Learning

TL;DR

This paper introduces ensemble distillation for federated learning, enabling flexible aggregation of heterogeneous models, leading to faster training with fewer communication rounds while maintaining privacy and efficiency.

Contribution

It proposes a novel ensemble distillation method for model fusion in federated learning, allowing aggregation of diverse models without structural constraints.

Findings

Faster training of server model with fewer communication rounds

Effective aggregation of heterogeneous models in FL

Maintains privacy and reduces communication costs

Abstract

Federated Learning (FL) is a machine learning setting where many devices collaboratively train a machine learning model while keeping the training data decentralized. In most of the current training schemes the central model is refined by averaging the parameters of the server model and the updated parameters from the client side. However, directly averaging model parameters is only possible if all models have the same structure and size, which could be a restrictive constraint in many scenarios. In this work we investigate more powerful and more flexible aggregation schemes for FL. Specifically, we propose ensemble distillation for model fusion, i.e. training the central classifier through unlabeled data on the outputs of the models from the clients. This knowledge distillation technique mitigates privacy risk and cost to the same extent as the baseline FL algorithms, but allows flexible aggregation over heterogeneous client models that can differ e.g. in size, numerical precision or structure. We show in extensive empirical experiments on various CV/NLP datasets (CIFAR-10/100, ImageNet, AG News, SST2) and settings (heterogeneous models/data) that the server model can be trained much faster, requiring fewer communication rounds than any existing FL technique so far.