A Personalized Federated Learning Algorithm: an Application in Anomaly Detection
This addresses data privacy and learning challenges in distributed systems like anomaly detection, but it is incremental as it builds on existing federated learning methods.
The paper tackles the problem of non-IID data in federated learning, which reduces central model accuracy, by proposing PC-FedAvg, a personalized algorithm that achieved higher accuracy than state-of-the-art methods in experiments on two datasets.
Federated Learning (FL) has recently emerged as a promising method that employs a distributed learning model structure to overcome data privacy and transmission issues paused by central machine learning models. In FL, datasets collected from different devices or sensors are used to train local models (clients) each of which shares its learning with a centralized model (server). However, this distributed learning approach presents unique learning challenges as the data used at local clients can be non-IID (Independent and Identically Distributed) and statistically diverse which decrease learning accuracy in the central model. In this paper, we overcome this problem by proposing a novel Personalized Conditional FedAvg (PC-FedAvg) which aims to control weights communication and aggregation augmented with a tailored learning algorithm to personalize the resulting models at each client. Our experimental validation on two datasets showed that our PC-FedAvg precisely constructed generalized clients' models and thus achieved higher accuracy compared to other state-of-the-art methods.