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Conference Aug 2026

Hybrid Federated Learning for Privacy-Preserving Intrusion Detection in Heterogeneous Network Environments

Centralized deep-learning intrusion detection systems (IDS) are accurate, but they scale badly and require raw traffic to be collected on one server, which creates privacy and communication problems. Federated learning (FL) avoids moving the data: clients train a shared model and exchange only updates. Its weakness is...

Rahul Nayak, Sanikommu Vasavi, S. Mohanty et al. · 0 citations
2026

Data Poisoning Detection for Secure Federated Learning With Dropout-Resilient Aggregation

Federated learning (FL) enables multiple clients to train a global model collaboratively without sharing their raw data. However, compromised clients can launch data poisoning attacks by submitting malicious local updates and thereby degrade model accuracy. In addition, unprotected model updates may disclose sensitive...

Jia-Yin Li, Sheng-Min Xu, Xing-Shuo Han et al. · 0 citations
Conference Aug 2026

DAD-FL: Defending Federated Learning Against Poisoning Attacks via Cosine Similarity and Gaussian Mixture Modeling

Federated learning (FL) enables collaborative model training without centralizing raw data, but its open participation mechanism makes the training process vulnerable to poisoning attacks. In realistic deployments, this problem is further aggravated by non-independent and identically distributed (Non-IID) data, because...

Chang-Wei Du, Shun-Rong Jiang · 0 citations
Conference Aug 2026

FedShield: A Dual Algorithm Defense Against Backdoor and Poisoning Attacks in Federated Learning

Federated Learning (FL) allows multiple clients to train a single model across devices, safeguarding data privacy in decentralised setups. However, while data does not leave the client, clients can still manipulate the model updates. This makes FL susceptible to poisoning and backdoor attacks. Backdoor attacks are part...

Rahul Patil, Tejas Vitthal Bhise, Prathamesh S. Nalawade et al. · 0 citations
Conference Aug 2026

FedShield: A Privacy-Preserving Federated Learning Framework for Behavioral Detection and Mitigation of Zero-Day Attacks

Zero-day is a type of attack that targets vulnerabilities unknown to vendors and security experts. Traditional signature-based intrusion detection systems fail to flag such attacks. ML models used to detect attacks require data aggregation, which raises substantial privacy concerns and infringes on an organization’s da...

Gargi Chaudhari, Zenia Nouphal, S. Vengurlekar · 0 citations
Sep 2026

Graph-aware Byzantine-resilient aggregation for adaptive poisoning detection in federated learning

GRAB-FL is proposed, a graph-aware, Byzantine-resilient FL framework for a bounded gray-box setting in which adversaries may observe global model trajectories and adapt their updates over time but cannot inspect server-side trust states.

Salam Fraihat, Yousef K. Sanjalawe, Qussai M. Yaseen et al. · 0 citations

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