Safe-FCL: Continual Risk Awareness and Fine-Grained Safety Aggregation for Federated Continual Learning of LLMs

Key points

• As federated tasks evolve, changing malicious update patterns challenge the ability of existing defenses to maintain reliable risk detection over time.1
• To translate risk awareness into aggregation-level safety control, the Fine-Grained Safety Aggregation (FGSA) mechanism combines client-level risk assessments from CRA with module-level deviations from historical update behavior to adaptively regulate aggregation contributions, suppressing potentially malicious updates while minimizing unnecessary restrictions on benign updates.1
• Extensive experiments demonstrate that Safe-FCL outperforms current SOTA methods, improving global-model Rule and MD-Judge scores in federated continual learning by 0.41% and 0.64%, respectively, while increasing average risk recognition accuracy by 2.01%.1

Sources (1)

• [1]Safe-FCL: Continual Risk Awareness and Fine-Grained Safety Aggregation for Federated Continual Learning of LLMs
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 10:50 AM

  Federated fine-tuning enables large language models (LLMs) to learn collaboratively from distributed client data, but malicious client updates can compromise global model safety.

  As federated tasks evolve, changing malicious update patterns challenge the ability of existing defenses to maintain reliable risk detection over time.

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