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backdoor attacks 1contrastive learning 1defense mechanisms 1federated learning 1model alignment 1

From the 1 of 3 linked papers with an AI index.

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3 papers

cs.CR2026

Defending Against Backdoor Attacks via Alignment Checking in Model-Contrastive Federated Learning

Hongliang Zhang, Zhongyuan Yu, Guijuan Wang +4

The paper proposes FedDAB, a two‑phase defense for federated learning that uses contrastive regularization and alignment checking to detect and exclude malicious local updates caus…

cs.LG2026

Towards Performance-Enhanced Model-Contrastive Federated Learning using Historical Information in Heterogeneous Scenarios

Hongliang Zhang, Jiguo Yu, Guijuan Wang +4

Federated Learning (FL) enables multiple nodes to collaboratively train a model without sharing raw data. However, FL systems are usually deployed in heterogeneous scenarios, where…

cs.LG2025

FastFT: Accelerating Reinforced Feature Transformation via Advanced Exploration Strategies

Tianqi He, Xiaohan Huang, Yi Du +6

Feature Transformation is crucial for classic machine learning that aims to generate feature combinations to enhance the performance of downstream tasks from a data-centric perspec…