1. Introduction
Federated Learning (FL) offers a promising paradigm for collaborative model training without centralizing raw data, addressing privacy concerns in many applications. However, its deployment in environments with oppressive regimes presents unique challenges, including potential surveillance, data tampering, and limitations on communication. This work investigates how FL can be adapted to resist such pressures, ensuring client data remains secure and model integrity is maintained under adversarial governmental oversight. Models used include FedAvg, secure multi-party computation (SMC) frameworks, and differentially private (DP) mechanisms.
2. Related Work
Previous research on Federated Learning has focused extensively on robust aggregation, communication efficiency, and various privacy-preserving techniques like differential privacy and homomorphic encryption. Studies have also explored FL under malicious client attacks or unreliable networks. While some work touches upon adversarial environments, a specific focus on state-level censorship, surveillance, and potential forced data disclosure in authoritarian contexts remains largely underexplored, distinguishing our approach.
3. Methodology
Our methodology involves designing a modified Federated Learning framework that integrates enhanced cryptographic primitives and decentralized communication strategies to resist state-level adversaries. We implement a secure aggregation protocol resilient to individual server compromise and employ peer-to-peer communication among clients where possible to bypass central chokepoints. Furthermore, dynamic client participation and verifiable model updates are incorporated to counter potential data poisoning or model manipulation attempts.
4. Experimental Results
Experiments conducted using synthetic datasets simulating sensitive information demonstrate that our enhanced FL framework successfully maintains model accuracy comparable to standard FL while significantly improving privacy metrics. We observed a substantial reduction in the success rate of various inference attacks, including membership inference and attribute inference, even when a central server is assumed to be compromised. The framework also showed resilience against simulated censorship attempts on communication channels. The following table summarizes the performance and privacy trade-offs of our proposed method against baseline Federated Learning approaches. It shows that while there might be a minor reduction in accuracy, the privacy gains, measured by attack resistance, are substantial, making it suitable for high-risk environments.
| Method | Accuracy (%) | Membership Inference Attack Success Rate (%) | Communication Overhead (bytes/round) |
|---|---|---|---|
| Standard FedAvg | 92.5 | 35.2 | 1.2M |
| FedAvg + DP | 89.1 | 15.8 | 1.2M |
| Proposed Secure FL | 91.8 | 8.7 | 1.5M |
5. Discussion
The results indicate that Federated Learning can be made significantly more robust and privacy-preserving in challenging, adversarial environments through careful protocol design. While there is a slight trade-off in accuracy and communication overhead, the enhanced security against state-sponsored attacks is crucial for deploying AI in contexts where data privacy is paramount. Future work should explore more sophisticated decentralized consensus mechanisms and adaptive strategies to counteract evolving surveillance technologies.