Blockchain-based Monitoring for Poison Attack Detection in Decentralized Federated Learning
Federated Learning (FL) is a machine learning technique that addresses the privacy challenges in terms of access rights of local datasets by enabling the training of a model across nodes holding their data samples locally. To achieve decentralized federated learning, blockchain-based FL was proposed as a distributed FL architecture. In decentralized FL, the chief is eliminated from the learning process as workers collaborate between each other to train the global model. Decentralized FL applications need to account for the additional delay incurred by blockchain-based FL deployments. Particularly in this setting, to detect targeted/untargeted poisoning attacks, we investigate the end-to-end learning completion latency of a realistic decentralized FL process protected against poisoning attacks. We propose a technique which consists in decoupling the monitoring phase from the detection phase in defenses against poisoning attacks in a decentralized federated learning deployment that aim at monitoring the behavior of the workers. We demonstrate that our proposed blockchain-based monitoring improved network scalability, robustness and time efficiency. The parallelization of operations results in minimized latency over the end-to-end communication, computation, and consensus delays incurred during the FL and blockchain operations.
Code (0)
등록된 구현이 없습니다.
Tasks
Federated LearningSimilar Papers 제목 키워드 기반
GFL: A Decentralized Federated Learning Framework Based On Blockchain
Federated learning(FL) is a rapidly growing field and many centralized and decentralized FL frameworks have been proposed. However, it is of great challenge for current FL frameworks to improve communication performance …
Data PoisoningFederated LearningFIDELIS: Blockchain-Enabled Protection Against Poisoning Attacks in Federated Learning
Federated learning enhances traditional deep learning by enabling the joint training of a model with the use of IoT device's private data. It ensures privacy for clients, but is susceptible to data poisoning attacks duri…
Federated LearningSecure Decentralized Learning with Blockchain
Federated Learning (FL) is a well-known paradigm of distributed machine learning on mobile and IoT devices, which preserves data privacy and optimizes communication efficiency. To avoid the single point of failure proble…
Federated LearningScaling Decentralized Learning with FLock
Fine-tuning the large language models (LLMs) are prevented by the deficiency of centralized control and the massive computing and communication overhead on the decentralized schemes. While the typical standard federated …
Domain GeneralizationFederated LearningAdversarial AttackEnhancing Split Learning with Sharded and Blockchain-Enabled SplitFed Approaches
Collaborative and distributed learning techniques, such as Federated Learning (FL) and Split Learning (SL), hold significant promise for leveraging sensitive data in privacy-critical domains. However, FL and SL suffer fr…
Federated Learning