Abnormal Client Behavior Detection in Federated Learning
In federated learning systems, clients are autonomous in that their behaviors are not fully governed by the server. Consequently, a client may intentionally or unintentionally deviate from the prescribed course of federated model training, resulting in abnormal behaviors, such as turning into a malicious attacker or a malfunctioning client. Timely detecting those anomalous clients is therefore critical to minimize their adverse impacts. In this work, we propose to detect anomalous clients at the server side. In particular, we generate low-dimensional surrogates of model weight vectors and use them to perform anomaly detection. We evaluate our solution through experiments on image classification model training over the FEMNIST dataset. Experimental results show that the proposed detection-based approach significantly outperforms the conventional defense-based methods.
Code (0)
등록된 구현이 없습니다.
Tasks
Anomaly DetectionFederated Learningimage-classificationImage ClassificationSimilar Papers 제목 키워드 기반
Anomalous Client Detection in Federated Learning
Federated learning (FL), with the growing IoT and edge computing, is seen as a promising solution for applications that are latency- and privacy-aware. However, due to the widespread dispersion of data across many client…
Anomaly DetectionEdge-computingFederated LearningFedNIA: Noise-Induced Activation Analysis for Mitigating Data Poisoning in FL
Federated learning systems are increasingly threatened by data poisoning attacks, where malicious clients compromise global models by contributing tampered updates. Existing defenses often rely on impractical assumptions…
Data PoisoningFederated LearningFedTADBench: Federated Time-Series Anomaly Detection Benchmark
Time series anomaly detection strives to uncover potential abnormal behaviors and patterns from temporal data, and has fundamental significance in diverse application scenarios. Constructing an effective detection model …
Anomaly DetectionFederated LearningTime SeriesTime Series Analysis+1Abnormal Local Clustering in Federated Learning
Federated learning is a model for privacy without revealing private data by transfer models instead of personal and private data from local client devices. While, in the global model, it's crucial to recognize each local…
ClusteringFederated LearningAnomaly Detection via Federated Learning
Machine learning has helped advance the field of anomaly detection by incorporating classifiers and autoencoders to decipher between normal and anomalous behavior. Additionally, federated learning has provided a way for …
Anomaly DetectionFederated LearningIntrusion Detection