When Clients Are Orchestrated: Strategic Gradient Manipulation to Defeat Federated Learning Servers with Efficient Defense

arXiv:2609.27124 · cs.CR, cs.AI · Submitted 2026-09-22 · Read on arXiv

cs.CR, cs.AI

Submitted: 2026-09-22

Updated: 2026-09-22

Terminology

• Accuracy — 47× in this paper · explained in 1 episode
• Aggregation — 27× in this paper · explained in 1 episode
• Federated Learning — 25× in this paper · explained in 14 episodes
• Robustness — 8× in this paper · explained in 5 episodes
• Consistency — 7× in this paper · explained in 3 episodes
• Clustering — 5× in this paper · explained in 2 episodes
• Fine-Tuning — 5× in this paper · explained in 5 episodes
• Communication Overhead — 4× in this paper · explained in 2 episodes
• Computational overhead — 4× in this paper · explained in 2 episodes
• Loss Function — 4× in this paper · explained in 2 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Ablation study — 2× in this paper · explained in 1 episode
• Benchmark — 2× in this paper · explained in 2 episodes
• Benchmarking — 2× in this paper · explained in 3 episodes
• Core — 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
• Regularization — 2× in this paper · explained in 3 episodes
• Transferability — 2× in this paper · explained in 4 episodes
• Actor — 1× in this paper · explained in 3 episodes
• Adaptability — 1× in this paper · explained in 1 episode
• Average Cost-Saving Percentage — 1× in this paper
• Benign Misclassification Rate — 1× in this paper
• Federated Learning (FL) — 1× in this paper · explained in 7 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Malicious Misclassification Rate — 1× in this paper
• Steering — 1× in this paper · explained in 2 episodes
• Stochastic Gradient Descent — 1× in this paper
• Stochastic Gradient Descent (SGD) — 1× in this paper · explained in 5 episodes
• Temporal consistency — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping
• Mitigating Sybils in Federated Learning Poisoning
• Maximal Brain Damage Without Data or Optimization: Disrupting Neural Networks via Sign-Bit Flips
• On the Convergence of FedAvg on Non-IID Data
• Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

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