Federated learning (FL) is known to be susceptible to model
poisoning attacks in which malicious clients hamper the accuracy
of the global model by sending manipulated model updates
to the central server during the FL training process. Existing
defenses mainly focus on Byzantine-robust FL aggregations,
and largely ignore the impact of the underlying deep
neural network (DNN) that is used to FL training. Inspired by
recent findings on critical learning periods (CLP) in DNNs,
where small gradient errors have irrecoverable impact on the
final model accuracy, we propose a new defense, called a
CLP-aware defense against poisoning of FL (DeFL). The key
idea of DeFL is to measure fine-grained differences between
DNN model updates via an easy-to-compute federated gradient
norm vector (FGNV) metric. Using FGNV, DeFL simultaneously
detects malicious clients and identifies CLP, which
in turn is leveraged to guide the adaptive removal of detected
malicious clients from aggregation. As a result, DeFL not
only mitigates model poisoning attacks on the global model
but also is robust to detection errors. Our extensive experiments
on three benchmark datasets demonstrate that DeFL
produces significant performance gain over conventional defenses
against state-of-the-art model poisoning attacks.
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FLAIR: Defense against Model Poisoning Attack in Federated Learning
Federated learning—multi-party, distributed learning in a decentralized environment—is vulnerable to model poisoning attacks, more so than centralized learning. This is because malicious clients can collude and send in carefully tailored model updates to make the global model inaccurate. This motivated the development of Byzantine-resilient federated learning algorithms, such as Krum, Bulyan, FABA, and FoolsGold. However, a recently developed untargeted model poisoning attack showed that all prior defenses can be bypassed. The attack uses the intuition that simply by changing the sign of the gradient updates that the optimizer is computing, for a set of malicious clients, a model can be diverted from the optima to increase the test error rate. In this work, we develop FLAIR—a defense against this directed deviation attack (DDA), a state-of-the-art model poisoning attack. FLAIR is based on ourintuition that in federated learning, certain patterns of gradient flips are indicative of an attack. This intuition is remarkably stable across different learning algorithms, models, and datasets. FLAIR assigns reputation scores to the participating clients based on their behavior during the training phase and then takes a weighted contribution of the clients. We show that where the existing defense baselines of FABA [IJCAI’19], FoolsGold [Usenix ’20], and FLTrust [NDSS ’21] fail when 20-30% of the clients are malicious, FLAIR provides byzantine-robustness upto a malicious client percentage of 45%. We also show that FLAIR provides robustness against even a white-box version of DDA.
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- Award ID(s):
- 2146449
- NSF-PAR ID:
- 10492607
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- ACM ASIA CCS
- Subject(s) / Keyword(s):
- Federated learning model poisoning Byzantine-robust aggregation
- Format(s):
- Medium: X
- Location:
- Melbourne
- Sponsoring Org:
- National Science Foundation
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