A Federated Learning Multi-Task Scheduling Mechanism Based on Trusted Computing Sandbox

Abstract

At present, some studies have combined federated learning with blockchain, so that participants can conduct federated learning tasks under decentralized conditions, sharing and aggregating model parameters. However, these schemes do not take into account the trusted supervision of federated learning and the case of malicious node attacks. This paper introduces the concept of a trusted computing sandbox to solve this problem. A federated learning multi-task scheduling mechanism based on a trusted computing sandbox is designed and a decentralized trusted computing sandbox composed of computing resources provided by each participant is constructed as a state channel. The training process of the model is carried out in the channel and the malicious behavior is supervised by the smart contract, ensuring the data privacy of the participant node and the reliability of the calculation during the training process. In addition, considering the resource heterogeneity of participant nodes, the deep reinforcement learning method was used in this paper to solve the resource scheduling optimization problem in the process of constructing the state channel. The proposed algorithm aims to minimize the completion time of the system and improve the efficiency of the system while meeting the requirements of tasks on service quality as much as possible. Experimental results show that the proposed algorithm has better performance than the traditional heuristic algorithm and meta-heuristic algorithm.

Task Scheduling Mechanisms for Fog Computing: A Systematic Survey

“…Liu et al [35] presented a federated learning-based multi-task scheduling mechanism for edge computing that utilizes trusted computing sandbox technology. The proposed mechanism aims to improve the efficiency and security of task scheduling in edge computing environments.…”

“…Researchers have identified that the presence of multiple edge devices with diverse computing power, network bandwidth, and varying security levels can be a potential threat to the security of task scheduling. To mitigate these security risks, various security mechanisms have been proposed to ensure the confidentiality, integrity, and availability of data in fog computing systems [35], [69]. However, these proposed solutions need to be tested and evaluated comprehensively to ensure their effectiveness and efficiency.…”

A Proposal for a Tokenized Intelligent System: A Prediction for an AI-Based Scheduling, Secured Using Blockchain

“…For the purpose of handling the problem of the unpredictability of tasks and QoS requirements of users in Fog Computing, the authors in [37] have proposed three different DRL-based solutions, with the aim of maximizing rewards with reference to resource utilization, designed as the Markov Decision Process, by taking into consideration the task priority, energy consumption, and latency as the QoS factors. Another effort has been made in [38] for a trusted supervision of federated learning and malicious node attacks by smart contract; they proposed the concept of a decentralized trusted computing sandbox, which is a federated learning multi-task scheduling mechanism using DRL to solve the resource scheduling optimization problem. Both of these two studies have not incorporated Blockchain in their proposed ideas of resolving the scheduling optimization problem.…”

FLIBD: A Federated Learning-Based IoT Big Data Management Approach for Privacy-Preserving over Apache Spark with FATE

“…To reduce private information exposure during the processing and transmission of components, HFL can employ homomorphic encryption systems [30][31][32], differential privacy mechanisms [33][34][35], and safe aggregation frameworks [36][37][38]. Other methods include blockchain-based FL [39,40] and multi-task FL [41,42].…”