Log In Sign Up

SphereFed: Hyperspherical Federated Learning

by   Xin Dong, et al.

Federated Learning aims at training a global model from multiple decentralized devices (i.e. clients) without exchanging their private local data. A key challenge is the handling of non-i.i.d. (independent identically distributed) data across multiple clients that may induce disparities of their local features. We introduce the Hyperspherical Federated Learning (SphereFed) framework to address the non-i.i.d. issue by constraining learned representations of data points to be on a unit hypersphere shared by clients. Specifically, all clients learn their local representations by minimizing the loss with respect to a fixed classifier whose weights span the unit hypersphere. After federated training in improving the global model, this classifier is further calibrated with a closed-form solution by minimizing a mean squared loss. We show that the calibration solution can be computed efficiently and distributedly without direct access of local data. Extensive experiments indicate that our SphereFed approach is able to improve the accuracy of multiple existing federated learning algorithms by a considerable margin (up to 6 communication efficiency across datasets and model architectures.


page 1

page 2

page 3

page 4


Survey of Personalization Techniques for Federated Learning

Federated learning enables machine learning models to learn from private...

Dual Attention-Based Federated Learning for Wireless Traffic Prediction

Wireless traffic prediction is essential for cellular networks to realiz...

Auto-weighted Robust Federated Learning with Corrupted Data Sources

Federated learning provides a communication-efficient and privacy-preser...

OCTOPUS: Overcoming Performance andPrivatization Bottlenecks in Distributed Learning

The diversity and quantity of the data warehousing, gathering data from ...

Federated Mutual Learning

Federated learning enables collaboratively training machine learning mod...

Exact Decomposition of Quantum Channels for Non-IID Quantum Federated Learning

Federated learning refers to the task of performing machine learning wit...

Knowledge-Aware Federated Active Learning with Non-IID Data

Federated learning enables multiple decentralized clients to learn colla...