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 …

联邦学习作为新兴的分布式机器学习解决了数据孤岛问题. 然而, 由于大规模,
分布式特性以及本地客户端的强自主性, 使得联邦学习极易遭受拜占庭攻击且攻击不易发现 …

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 …

Federated learning (FL) is emerging as a sought-after distributed machine learning
architecture, offering the advantage of model training without direct exposure to raw data …