Titlebook: Handbook of Trustworthy Federated Learning; My T. Thai,Hai N. Phan,Bhavani Thuraisingham Book 2025 The Editor(s) (if applicable) and The A

誓約

書目名稱Handbook of Trustworthy Federated Learning影響因子(影響力)




書目名稱Handbook of Trustworthy Federated Learning影響因子(影響力)學(xué)科排名




書目名稱Handbook of Trustworthy Federated Learning網(wǎng)絡(luò)公開度




書目名稱Handbook of Trustworthy Federated Learning網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Handbook of Trustworthy Federated Learning被引頻次




書目名稱Handbook of Trustworthy Federated Learning被引頻次學(xué)科排名




書目名稱Handbook of Trustworthy Federated Learning年度引用




書目名稱Handbook of Trustworthy Federated Learning年度引用學(xué)科排名




書目名稱Handbook of Trustworthy Federated Learning讀者反饋




書目名稱Handbook of Trustworthy Federated Learning讀者反饋學(xué)科排名

可卡

1931-6828 and practice communities towards adapting federated learning.This handbook aims to serve as a one-stop, reliable resource, including curated surveys and expository contributions on federated learning. It covers a comprehensive range of topics, providing the reader with technical and non-technical fu

FACT

A Two-Stage Stochastic Programming Approach for the Key Management ,-Composite Schemeroduce a desired level of communication security in settings where the network topology is unknown in advance. The model enables secure encryption strategies that are resilient against node capture, failures, and network topology changes. We present computational studies to demonstrate the efficacy of the proposed scheme.

智力高

Federated Bilevel Optimizationtate-of-the-art (SOTA) algorithms, and recent advances in federated bilevel optimization. Especially, this chapter demonstrates how SOTA algorithms approximate hypergradient under different settings to make federated bilevel optimization feasible.

Vasodilation

Robust Federated Learning for Edge Intelligenceramount. It is imperative to ensure the transparency, accountability, and fairness of AI systems to foster their social acceptance and adoption, mitigate their risks and harms, and maximize their benefits and opportunities.

使饑餓

Aggressive

Secure Federated Learningoach to detect and mitigate attacks. In phase I, LoMar scores model updates based on the relative distribution over neighboring participants using kernel density estimation. In phase II, an optimal threshold is approximated to distinguish between malicious and clean updates. Extensive experiments on

品嘗你的人

Data Poisoning and Leakage Analysis in Federated Learningtrimental damage on the performance of the global model. We will categorize and compare representative poisoning attacks and the effectiveness of their mitigation techniques, delivering an in-depth understanding of the negative impact of data poisoning. Finally, we demonstrate the potential of dynam

調(diào)整校對(duì)

Robust Federated Learning Against Targeted Attackers Using Model Updates Correlation similarity based algorithms in distributed attack settings are then acknowledged. To combat these attacks, we introduce a divergence-based algorithm called Div-DBAD and establish its superiority on distributed backdoor attacks done on the setup. Experimental analysis on two standard machine learnin

justify

Unfair Trojan: Targeted Backdoor Attacks Against Model Fairnessfairness. This chapter demonstrates a novel and flexible attack, which we call Unfair Trojan, which aims to target model fairness while remaining stealthy. Using this attack, an adversary can have devastating effects against machine learning models, increasing their demographic parity, a key fairnes