Differentially private federated learning with Byzantine-robust aggregation: A cross-domain framework for secure model training in banking and healthcare systems

arXiv:2609.03064 · cs.CR, cs.LG · Submitted 2026-09-02 · Read on arXiv

cs.CR, cs.LG

Submitted: 2026-09-02

Updated: 2026-09-02

Terminology

• Aggregation — 25× in this paper · explained in 1 episode
• Federated Learning — 20× in this paper · explained in 14 episodes
• Robustness — 14× in this paper · explained in 5 episodes
• Accuracy — 13× in this paper · explained in 1 episode
• Differential Privacy — 13× in this paper · explained in 4 episodes
• Differentially Private — 2× in this paper · explained in 3 episodes
• Advanced Artificial Intelligence — 1× in this paper
• Artificial Intelligence (AI) — 1× in this paper · explained in 2 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• alpha — 1× in this paper · explained in 1 episode

Sources

• Federated Learning: Strategies for Improving Communication Efficiency
• Threats to Federated Learning: A Survey
• Differentially Private Federated Learning: A Client Level Perspective

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