Federated Multi-Task Learning under a Mixture of Distributions

By Othmane Marfoq, Giovanni Neglia, Aurélien Bellet, Laetitia Kameni, Richard Vidal. Neural Information Processing Systems, 2021

398 citations recorded by the source corpus.

The increasing size of data generated by smartphones and IoT devices motivated the development of Federated Learning (FL), a framework for on-device collaborative training of machine learning models. First efforts in FL focused on learning a single global model with good average performance across clients, but the global model may be arbitrarily bad for a given client, due to the inherent heterogeneity of local data distributions. Federated multi-task learning (MTL) approaches can learn personalized models by formulating an opportune penalized optimization problem. The penalization term can capture complex relations among personalized models, but eschews clear statistical assumptions about local data distributions. In this work, we propose to study federated MTL under the flexible assumption that each local data distribution is a mixture of unknown underlying distributions. This assumption encompasses most of the existing personalized FL approaches and leads to federated EM-like algorithms for both client-server and fully decentralized settings. Moreover, it provides a principled way to serve personalized models to clients not seen at training time. The algorithms' convergence is analyzed through a novel federated surrogate optimization framework, which can be of general interest. Experimental results on FL benchmarks show that our approach provides models with higher accuracy and fairness than state-of-the-art methods.

Subfields: Artificial Intelligence, Computer Science Applications

View Paper: https://www.semanticscholar.org/paper/73e3a0370c1229ce1804058e0c048c2dc81ebe9f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Bo Du
2. Mang Ye
3. Pong Chi Yuen
4. Dacheng Tao
5. Xiuwen Fang
6. Jing Zhang
7. Charu C. Aggarwal
8. Xinlei Chen

In Computer Science Applications

1. Dacheng Tao
2. Bo Du
3. Mang Ye

Citations in the corpus

• Heterogeneous Federated Learning: State-of-the-art and Research Challenges (2023, background): “[152] study FMTL under the assumption that each local data distribution is a mixture of unknown underlying distributions.”