Federated learning (FL) is a distributed optimization paradigm that learns from data samples
distributed across a number of clients. Adaptive client selection that is cognizant of the …

Decentralized federated learning (DFL) facilitates collaborative model training across
multiple connected clients without a central coordination server thereby avoiding the single …

Federated Learning (FL) is increasingly vulnerable to model poisoning attacks, where
malicious clients degrade the global model's accuracy with manipulated updates …

In the modern interconnected world, the popularization of networks and the rapid
development of information technology led to the increasing security risks and threats in …

Federated learning (FL) is crucial in edge computing for next-generation wireless networks
because it enables collaborative learning among devices while protecting data privacy …

Software defect prediction technology can discover potential errors or hidden defects by
establishing prediction models before the use of products in the field of software …

Most existing model poisoning attacks in federated learning (FL) control a set of malicious
clients and share a fixed number of malicious gradients with the server in each FL training …

Without direct access to the client's data, federated learning (FL) is well-known for its unique
strength in data privacy protection among existing distributed machine learning techniques …

There is a exponential growth of data usage, specially due to the proliferation of connected
applications with personalized models for different applications. In this context, Federated …