Journal article

Towards Fair and Privacy-Preserving Federated Deep Models

L Lyu, J Yu, K Nandakumar, Y Li, X Ma, J Jin, H Yu, KS Ng

IEEE Transactions on Parallel and Distributed Systems | IEEE | Published : 2020

Abstract

The current standalone deep learning framework tends to result in overfitting and low utility. This problem can be addressed by either a centralized framework that deploys a central server to train a global model on the joint data from all parties, or a distributed framework that leverages a parameter server to aggregate local model updates. Server-based solutions are prone to the problem of a single-point-of-failure. In this respect, collaborative learning frameworks, such as federated learning (FL), are more robust. Existing federated learning frameworks overlook an important aspect of participation: fairness. All parties are given the same final model without regard to their contributions..

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University of Melbourne Researchers

Grants

Awarded by Australian Research Council


Funding Acknowledgements

This work was supported, in part, by IBM PhD Fellowship; ANUTranslational Fellowship; Nanyang Assistant Professorship (NAP); and NTU-WeBank JRI (NWJ-2019-007). The authors would like to thank Prof. Benjamin Rubinstein, Dr. Kumar Bhaskaran, and Prof. Marimuthu Palaniswami for their insightful discussions. This research was undertaken using the LIEF HPC-GPGPU Facility hosted at the University of Melbourne. This Facility was established with the assistance of LIEF Grant LE170100200.