Differential Privacy Trade-offs in Federated Code Generation Model Fine-Tuning

Abstract

This report synthesises findings from 3 peer-reviewed papers addressing the following research question: What is the trade-off between inference latency and model robustness against adversarial attacks when applying differential privacy mechanisms to federated fine-tuning of code generation models. Federated learning (FL) is a machine learning setting where many clients (e.g., mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g., service provider), while keeping the training data decentralized. FL embodies. 10 claims were extracted from source literature; 10 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.2/10. This report is a machine-generated literature synthesis and does not constitute original research. Research goal: What is the trade-off between inference latency and model robustness against adversarial attacks when applying differential privacy mechanisms to federated fine-tuning of code generation models? Autonomous literature synthesis. Automated review score: 8.2/10. Full text and citation available at Assignee Research.