A Secure Federated Learning Framework for 5G Networks (2005.05752v1)

Abstract: Federated Learning (FL) has been recently proposed as an emerging paradigm to build machine learning models using distributed training datasets that are locally stored and maintained on different devices in 5G networks while providing privacy preservation for participants. In FL, the central aggregator accumulates local updates uploaded by participants to update a global model. However, there are two critical security threats: poisoning and membership inference attacks. These attacks may be carried out by malicious or unreliable participants, resulting in the construction failure of global models or privacy leakage of FL models. Therefore, it is crucial for FL to develop security means of defense. In this article, we propose a blockchain-based secure FL framework to create smart contracts and prevent malicious or unreliable participants from involving in FL. In doing so, the central aggregator recognizes malicious and unreliable participants by automatically executing smart contracts to defend against poisoning attacks. Further, we use local differential privacy techniques to prevent membership inference attacks. Numerical results suggest that the proposed framework can effectively deter poisoning and membership inference attacks, thereby improving the security of FL in 5G networks.

A Secure Federated Learning Framework for 5G Networks

The paper "A Secure Federated Learning Framework for 5G Networks" presents a novel approach to enhancing the security of Federated Learning (FL) in 5G infrastructures. FL, a distributed machine learning paradigm, enables model training using localized data stored on various devices while upholding data privacy. It addresses privacy concerns by ensuring that raw data isn't shared with central servers, thus preserving the confidentiality of sensitive information during the training phase. However, the FL environment faces prominent security threats, specifically poisoning attacks and membership inference attacks.

Key Contributions

The authors propose a blockchain-based framework designed to mitigate these security threats in FL systems:

1. Smart Contracts for Poisoning Attack Mitigation: The paper introduces a blockchain-powered mechanism involving smart contracts aimed at deterring poisoning attacks. In FL, malicious participants could submit false data to disrupt the model training process. By leveraging Ethereum-based smart contracts, the central aggregator can automatically identify and prevent contributions from unreliable devices, using the contract for reputation management and reward distribution according to model quality.
2. Local Differential Privacy for Membership Inference Protection: To counteract membership inference attacks—where adversaries might infer sensitive input data values from shared model gradients—the proposed framework integrates a local differential privacy technique. This method perturbs the model updates with controlled noise, making it computationally infeasible for attackers to decipher private information from model parameters.

Numerical Results and Performance Analysis

Numerical evaluations suggest that the innovative framework effectively mitigates both poisoning and membership inference attacks, providing enhanced security for federated models in 5G networks. The approach maintains high model accuracy while protecting participant privacy, demonstrating a trade-off between privacy assurance and performance efficacy. Experiments using data sets like MNIST and CIFAR-10 validate these capabilities, with well-controlled parameters mitigating unwanted noise impact due to differential privacy implementations.

Implications and Future Directions

This secure FL framework offers substantial advances in constructing trustless, incentive-driven marketplaces for collaborative machine learning activities across distributed networks, such as 5G. Aside from practical gains, this system theoretically enriches blockchain’s application in privacy-centric computing ecosystems. However, challenges persist, particularly regarding computational efficiency, fairness in reward distribution, and managing communication overheads in expansive networks. Addressing these concerns will further refine federated models’ reliability and scalability—a promising endeavor for future AI developments.

In conclusion, by integrating blockchain's decentralized principles with FL's privacy-preserving needs, this work sets the stage for more robust AI systems in the era of pervasive computing. Continued research should explore optimization techniques to balance privacy and performance, enhance algorithmic fairness, and refine consensus protocols in blockchain-based learning frameworks.