To counter this issue, personalized FL (PFL) was proposed to produce dedicated local models for each individual user.
Federated learning (FL) is particularly vulnerable to heterogeneously distributed data, since a common global model in FL may not adapt to the heterogeneous data distribution of each user.
With astonishing speed, bandwidth, and scale, Mobile Edge Computing (MEC) has played an increasingly important role in the next generation of connectivity and service delivery.
The results show that the selective behaviour of our algorithm leads to a significant reduction in the number of communication rounds and the amount of time (up to 2. 4x speedup) for the global model to converge and also provides accuracy gain.
Federated Learning (FL), arising as a privacy-preserving machine learning paradigm, has received notable attention from the public.
The client selection policy is critical to an FL process in terms of training efficiency, the final model's quality as well as fairness.
In this paper, a multi-layer federated learning protocol called HybridFL is designed for the MEC architecture.
Federated learning (FL) has attracted increasing attention as a promising approach to driving a vast number of end devices with artificial intelligence.
In light of this, we have developed a prediction-driven, unsupervised anomaly detection scheme, which adopts a backbone model combining the decomposition and the inference of time series data.