Secure federated learning and GAN framework for poisoning defense and stable adversarial training

Background

Federated learning allows multiple clients to collaboratively train models while keeping data local. As industries from healthcare to finance adopt FL to meet privacy and regulatory requirements, distributed machine-learning architectures and generative modeling frameworks like GANs are increasingly important. However, decentralized training introduces unique vulnerabilities. Without centralized data visibility, malicious clients can inject poisoned data or corrupted model updates, undermining model integrity and threatening privacy guarantees. As data privacy regulations tighten, federated learning is expanding into sensitive applications like personalized medicine and edge AI.

Existing defense strategies often rely on global parameter obfuscation, secure aggregation, or post hoc anomaly detection, which can be computationally intensive and degrade model accuracy. Many anomaly detectors assume specific data distributions and fail to generalize across heterogeneous client data, leaving systems exposed to stealthy poisoning attacks. Similarly, traditional GANs suffer from unstable training dynamics, mode collapse, and vanishing gradients, especially in distributed settings where synchronization latency and non-IID data exacerbate divergence. These limitations hinder reliable convergence and expose federated systems to performance degradation and security breaches.

Technology

The described system integrates dual-layer defenses at each federated learning client: an input defense that either applies an optimized ensemble of filters—such as Sobel, Gaussian blur, or sharpening—or leverages a robust neural network to block or reconstruct poisoned samples, and an output defense employing negative selection–based anomaly filters to detect and discard or correct corrupted model updates prior to aggregation. In parallel, a distributed GAN framework partitions generator and discriminator components into multiple substructures, enhancing training stability, accelerating convergence, and mitigating mode collapse without dependence on specific data distributions or model architectures.

This approach stands out by offering a comprehensive, client-level defense against both data and model poisoning, eliminating the need for heavy server-side aggregation obfuscation. The input filter ensemble is optimized via formulated resilience metrics, while the neural discriminator can reconstruct or reject malicious inputs, and the negative selection output filters focus on anomalies rather than known attack patterns, enhancing obfuscation and thwarting reverse engineering. Moreover, distributing GAN components across multiple substructures addresses traditional GAN limitations—unstable training, mode collapse, vanishing gradients—resulting in faster convergence and broader applicability across domains without model- or data-specific tuning.

Benefits

• End-to-end protection against data and model poisoning via client-side input and output defenses
• Domain- and model-agnostic filtering framework, enabling cross-domain applicability
• Negative selection–based anomaly filters that detect malicious updates and resist reverse engineering
• Optimized ensembles of image/data filters or robust neural discriminators to block or reconstruct corrupted inputs
• Real-time correction or discarding of tainted local model updates before aggregation
• Distributed GAN architecture enhancing training stability, convergence, and resilience to mode collapse
• Lightweight integration into existing federated learning platforms without significant computational overhead

Applications

• Secure healthcare data collaboration
• Federated finance fraud detection
• Edge IoT anomaly detection
• Biometric identity verification
• Autonomous vehicle data sharing

Additional info

Each federated client embeds an input defense layer—either an optimized ensemble of data filters (e.g., Sobel, Gaussian blur, sharpening) or a robust neural discriminator—to block or reconstruct poisoned samples, and an output defense layer using negative-selection anomaly filters to detect and discard corrupted model updates before aggregation. Simultaneously, a distributed GAN architecture partitions generator and discriminator components across multiple substructures, improving training stability, convergence, and resistance to mode collapse.