Upholding Robustness in Federated Learning: Trends, Emerging Strategies, and Research Opportunities
cs.LG
Submitted: 2026-09-23
Updated: 2026-09-23
Terminology
Sources
- Backdoor Attacks on Federated Meta-Learning
- Performance Analysis and Evaluation of Post Quantum Secure Blockchained Federated Learning
- Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data
- FedCC: Robust Federated Learning against Model Poisoning Attacks
- OASIS: Offsetting Active Reconstruction Attacks in Federated Learning
- Improving Federated Learning Personalization via Model Agnostic Meta Learning
- FastSecAgg: Scalable Secure Aggregation for Privacy-Preserving Federated Learning
- Adversarial Machine Learning at Scale
- Model Extraction Attacks on Split Federated Learning
- Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challenges
- A Survey on Federated Learning Poisoning Attacks and Defenses
- Backdoor attacks and defenses in feature-partitioned collaborative learning
- Understanding Reconstruction Attacks with the Neural Tangent Kernel and Dataset Distillation
- Threats to Federated Learning: A Survey
- Fake or Compromised? Making Sense of Malicious Clients in Federated Learning
- Contextual Model Aggregation for Fast and Robust Federated Learning in Edge Computing
- Mean Aggregator is More Robust than Robust Aggregators under Label Poisoning Attacks on Distributed Heterogeneous Data
- A Model Consistency-Based Countermeasure to GAN-Based Data Poisoning Attack in Federated Learning
- Can You Really Backdoor Federated Learning?
- Subject Membership Inference Attacks in Federated Learning
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