RobustFed: A Truth Inference Approach for Robust Federated Learning
- URL: http://arxiv.org/abs/2107.08402v1
- Date: Sun, 18 Jul 2021 09:34:57 GMT
- Title: RobustFed: A Truth Inference Approach for Robust Federated Learning
- Authors: Farnaz Tahmasebian, Jian Lou, and Li Xiong
- Abstract summary: Federated learning is a framework that enables clients to train a collaboratively global model under a central server's orchestration.
The aggregation step in federated learning is vulnerable to adversarial attacks as the central server cannot manage clients' behavior.
We propose a novel robust aggregation algorithm inspired by the truth inference methods in crowdsourcing.
- Score: 9.316565110931743
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Federated learning is a prominent framework that enables clients (e.g.,
mobile devices or organizations) to train a collaboratively global model under
a central server's orchestration while keeping local training datasets'
privacy. However, the aggregation step in federated learning is vulnerable to
adversarial attacks as the central server cannot manage clients' behavior.
Therefore, the global model's performance and convergence of the training
process will be affected under such attacks.To mitigate this vulnerability
issue, we propose a novel robust aggregation algorithm inspired by the truth
inference methods in crowdsourcing via incorporating the worker's reliability
into aggregation. We evaluate our solution on three real-world datasets with a
variety of machine learning models. Experimental results show that our solution
ensures robust federated learning and is resilient to various types of attacks,
including noisy data attacks, Byzantine attacks, and label flipping attacks.
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