Anomaly Detection through Unsupervised Federated Learning
- URL: http://arxiv.org/abs/2209.04184v1
- Date: Fri, 9 Sep 2022 08:45:47 GMT
- Title: Anomaly Detection through Unsupervised Federated Learning
- Authors: Mirko Nardi, Lorenzo Valerio, Andrea Passarella
- Abstract summary: Federated learning is proving to be one of the most promising paradigms for leveraging distributed resources.
We propose a novel method in which, through a preprocessing phase, clients are grouped into communities.
The resulting anomaly detection model is then shared and used to detect anomalies within the clients of the same community.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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