Anomaly Detection through Unsupervised Federated Learning

by   Mirko Nardi, et al.

Federated learning (FL) is proving to be one of the most promising paradigms for leveraging distributed resources, enabling a set of clients to collaboratively train a machine learning model while keeping the data decentralized. The explosive growth of interest in the topic has led to rapid advancements in several core aspects like communication efficiency, handling non-IID data, privacy, and security capabilities. However, the majority of FL works only deal with supervised tasks, assuming that clients' training sets are labeled. To leverage the enormous unlabeled data on distributed edge devices, in this paper, we aim to extend the FL paradigm to unsupervised tasks by addressing the problem of anomaly detection in decentralized settings. In particular, we propose a novel method in which, through a preprocessing phase, clients are grouped into communities, each having similar majority (i.e., inlier) patterns. Subsequently, each community of clients trains the same anomaly detection model (i.e., autoencoders) in a federated fashion. The resulting model is then shared and used to detect anomalies within the clients of the same community that joined the corresponding federated process. Experiments show that our method is robust, and it can detect communities consistent with the ideal partitioning in which groups of clients having the same inlier patterns are known. Furthermore, the performance is significantly better than those in which clients train models exclusively on local data and comparable with federated models of ideal communities' partition.


A Personalized Federated Learning Algorithm: an Application in Anomaly Detection

Federated Learning (FL) has recently emerged as a promising method that ...

Prior-Independent Auctions for the Demand Side of Federated Learning

Federated learning (FL) is a paradigm that allows distributed clients to...

Federated Learning from Only Unlabeled Data with Class-Conditional-Sharing Clients

Supervised federated learning (FL) enables multiple clients to share the...

Privacy-preserving Anomaly Detection in Cloud Manufacturing via Federated Transformer

With the rapid development of cloud manufacturing, industrial production...

FedDis: Disentangled Federated Learning for Unsupervised Brain Pathology Segmentation

In recent years, data-driven machine learning (ML) methods have revoluti...

Failure-tolerant Distributed Learning for Anomaly Detection in Wireless Networks

The analysis of distributed techniques is often focused upon their effic...

ARIBA: Towards Accurate and Robust Identification of Backdoor Attacks in Federated Learning

The distributed nature and privacy-preserving characteristics of federat...

Please sign up or login with your details

Forgot password? Click here to reset