AntiFLipper: A Secure and Efficient Defense Against Label-Flipping Attacks in Federated Learning

arXiv:2509.22873 · cs.CR · Submitted 2025-09-26 · Read on arXiv

cs.CR

Submitted: 2025-09-26

Updated: 2026-09-24

Code: https://github.com/abidh8820/AntiFlipper

Terminology

• Accuracy — 43× in this paper · explained in 1 episode
• Federated Learning — 33× in this paper · explained in 14 episodes
• Aggregation — 31× in this paper · explained in 1 episode
• Robustness — 10× in this paper · explained in 5 episodes
• Computational overhead — 5× in this paper · explained in 2 episodes
• Test Error — 5× in this paper
• Core — 3× in this paper · explained in 2 episodes
• Federated Learning (FL) — 3× in this paper · explained in 7 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Data Heterogeneity — 2× in this paper · explained in 3 episodes
• Non-IID Data — 2× in this paper · explained in 2 episodes
• Adaptability — 1× in this paper · explained in 1 episode
• Clustering — 1× in this paper · explained in 2 episodes
• Communication Overhead — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Decentralized Learning — 1× in this paper · explained in 2 episodes
• Differential Privacy — 1× in this paper · explained in 4 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Modular Components — 1× in this paper · explained in 2 episodes

Sources

• Threats to Federated Learning: A Survey
• FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping

Related papers

• SoK: AI-Augmented Binary Reversing
• Relaxed Sender Anonymity for CBDC Interbank Settlement: A Zero-Knowledge Approach on Permissioned EVM
• Calibration-Family Overfit: Why Trusted Sabotage Monitors Don't Transfer Across Lineages
• Efficient Fuzzy PSI under One-Sided Assumptions
• Sealing the Audit-Runtime Gap for LLM Skills
• Token Composition: A Graph Based on EVM Logs