Brittle
发表于 2025-3-26 22:04:58
Trustworthiness, Privacy, and Security in Federated Learninghe regulatory requirements of laws and regulations are becoming increasingly strict, bringing huge impacts and challenges to enterprises with user’s personal data such as Internet services and financial technology. Up to a point, federal learning ensures data privacy by storing and processing person
tariff
发表于 2025-3-27 03:09:30
Secure Federated Learningt sharing their raw data. It addresses the challenge of data privacy in distributed environments by allowing data to remain decentralized while still benefiting from the collective knowledge. However, due to this collaborative training of a shared model, it has been known that FL is susceptible to v
Barrister
发表于 2025-3-27 09:01:17
http://reply.papertrans.cn/44/4311/431035/431035_33.png
Needlework
发表于 2025-3-27 12:39:47
http://reply.papertrans.cn/44/4311/431035/431035_34.png
言外之意
发表于 2025-3-27 17:03:39
Unfair Trojan: Targeted Backdoor Attacks Against Model Fairness they are becoming increasingly vulnerable to various forms of attacks, such as backdoor and data poisoning attacks that can have adverse effects on model behavior. These attacks become more prevalent and complex in federated learning, where multiple local models contribute to a single global model
uveitis
发表于 2025-3-27 21:22:08
Federated Bilevel Optimizationsearch, meta learning, etc. To facilitate those machine learning models to federated learning, federated bilevel optimization has been actively studied recently. To deepen the understanding of federated bilevel optimization and advance its development, this chapter discusses the unique challenges, s
命令变成大炮
发表于 2025-3-27 22:22:36
http://reply.papertrans.cn/44/4311/431035/431035_37.png
Optometrist
发表于 2025-3-28 05:10:18
http://reply.papertrans.cn/44/4311/431035/431035_38.png
abracadabra
发表于 2025-3-28 09:11:18
Privacy in Federated Learning Natural Language Models individual training examples, which severely affects the privacy and security of private datasets. In this chapter, we will discuss training language models in Federated Learning and its privacy and security challenges of the training process. We introduce a novel concept of user-entity differentia
游行
发表于 2025-3-28 12:52:53
Federated Learning of Models Pretrained on Different Features with Consensus Graphsce. Existing federated learning paradigms enable this via model aggregation that enforces a strong form of modeling homogeneity and synchronicity across clients. This is however not suitable to many practical scenarios. For example, in distributed sensing, heterogeneous sensors reading data from dif