Towards Secure and Robust Federated Learning

Decentralized and Secure Aggregation in Federated Learning

Federated learning (FL) is a machine learning technique where multiple clients with local data collaborate in training a machine learning model. However, this centralization of sensitive model updates incurs security and privacy issues. A malicious aggregator can launch inference attacks to violate the privacy of clients' private data from local models and explore sensitive knowledge from global model. Trusted Execution Environment (TEE) based schemes offer secure enclaves to protect models privacy during the aggregation procedure. However, TEE technologies incur new functional and security assumptions, where a single vulnerability could undermine the trusted system. Cryptographic schemes such as homomorphic encryption (HE), differential privacy (DP) and secret sharing (SS) cannot achieve the simultaneous requirements of efficiency, accuracy and privacy currently. In this paper, we present DoshFL that avoids expensive cryptographic operations and enables tunable trade-off between privacy and efficiency in federated learning for model asset protection.

FedGuard: A Federated Learning Framework for Collaborative and Privacy-Preserving Mobile Threat Intelligence

A Comprehensive Defense Framework Against Poisoning Backdoor Attacks in Federated Learning

This work employs the novel dimensionality reduction technique UMAP and a stringent filtering mechanism to effectively identify and exclude potential malicious participants without relying on traditional noise addition methods and demonstrates that the proposed method maintains high main task accuracy while effectively mitigating backdoor attacks across various attack scenarios.

An Approach Utilizing Frequency Analysis to Counteract Poisoning Attacks in Federated Learning

Machine learning requires diverse training datasets from multiple clients for improved performance. However, sharing datasets is often a legal and privacy issue across countries and organizations. Federated Learning (FL) is a machine learning framework allowing individual clients to train datasets locally and share only the weight updates to a central server where the updates are aggregated. FL addresses the security and privacy issues concerned with data-sharing; however, it is vulnerable to poisoning attacks where a malicious client can purposefully alter the model updates. Even a smallest input deviation can exploit the system leading to misclassification. In this study, we propose a lightweight defense mechanism for mitigating poisoning attacks in Federated Learning (FL) systems. Our approach involves transforming model weights into the frequency domain to identify core frequency components containing sufficient model weight information. Additionally, we employ a model filtering algorithm to predict poisoning attacks based on the output of the frequency analysis method. This enables effective filtering of malicious updates during local training on client devices. Our proposed defense mechanism enhances the security and integrity of FL systems against adversarial attack ensuring secure model aggregation.

Federated Learning in Cybersecurity: Enhancing Decentralized Threat Detection

This review underscores the potential of FL to become a foundational technology in next-generation cybersecurity systems, enabling scalable and privacy-preserving threat mitigation across distributed infrastructures.

SecureFedShield: An Adaptive Privacy-Preserving Federated Defense Framework Against Adversarial Attacks in Financial Fraud Detection

SecureFedShield is proposed, a privacy-preserving federated learning framework designed for secure financial fraud detection in adversarial environments that integrates adaptive privacy protection, trust-aware client evaluation, adversarial update detection, and robust model aggregation into a unified architecture.