Robust Aggregation for Federated Learning

We present a novel approach to federated learning that endows its aggregation process with greater robustness to potential poisoning of local data or model parameters of participating devices. The proposed approach, Robust Federated Aggregation (RFA), relies on the aggregation of updates using the g...

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Bibliographic Details
Published in:	IEEE transactions on signal processing Vol. 70; pp. 1142 - 1154
Main Authors:	Pillutla, Krishna, Kakade, Sham M., Harchaoui, Zaid
Format:	Journal Article
Language:	English
Published:	New York IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Agglomeration Aggregates Algorithms Collaborative work Computer vision corrupted updates Corruption data privacy distributed learning Federated learning Machine learning Natural language processing Optimization Privacy robust aggregation Robustness Servers Signal processing algorithms
ISSN:	1053-587X, 1941-0476
Online Access:	Get full text
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