A Federated Learning Framework with Blockchain-Based Auditable Participant Selection

Federated learning is an important distributed model training technique in Internet of Things(IoT),in which participant selection is a key component that plays a role in improving training efficiency and model accuracy.This module enables a central server to select a subset of participants to performmodel training based on data and device information.By doing so,selected participants are rewarded and actively perform model training,while participants that are detrimental to training efficiency and model accuracy are excluded.However,in practice,participants may suspect that the central server may have miscalculated and thus not made the selection honestly.This lack of trustworthiness problem,which can demotivate participants,has received little attention.Another problem that has received little attention is the leakage of participants’private information during the selection process.We will therefore propose a federated learning framework with auditable participant selection.It supports smart contracts in selecting a set of suitable participants based on their training loss without compromising the privacy.Considering the possibility of malicious campaigning and impersonation of participants,the framework employs commitment schemes and zero-knowledge proofs to counteract these malicious behaviors.Finally,we analyze the security of the framework and conduct a series of experiments to demonstrate that the framework can effectively improve the efficiency of federated learning.

A Survey on Task and Participant Matching in Mobile Crowd Sensing

Mobile crowd sensing is an innovative paradigm which leverages the crowd, i.e., a large group of people with their mobile devices, to sense various information in the physical world. With the help of sensed information, many tasks can be fulfilled in an efficient manner, such as environment monitoring, traffic prediction, and indoor localization. Task and participant matching is an important issue in mobile crowd sensing, because it determines the quality and efficiency of a mobile crowd sensing task. Hence, numerous matching strategies have been proposed in recent research work. This survey aims to provide an up-to-date view on this topic. We propose a research framework for the matching problem in this paper, including participant model, task model, and solution design. The participant model is made up of three kinds of participant characters, i.e., attributes, requirements, and supplements. The task models are separated according to application backgrounds and objective functions. Offline and online solutions in recent literatures are both discussed. Some open issues are introduced, including matching strategy for heterogeneous tasks, context-aware matching, online strategy, and leveraging historical data to finish new tasks.

Joint Participant Selection and Learning Optimization for Federated Learning of Multiple Models in Edge Cloud

To overcome the limitations of long latency and privacy concerns from cloud computing,edge computing along with distributed machine learning such as federated learning(FL),has gained much attention and popularity in academia and industry.Most existing work on FL over the edge mainly focuses on optimizing the training of one shared global model in edge systems.However,with the increasing applications of FL in edge systems,there could be multiple FL models from different applications concurrently being trained in the shared edge cloud.Such concurrent training of these FL models can lead to edge resource competition(for both computing and network resources),and further affect the FL training performance of each other.Therefore,in this paper,considering a multi-model FL scenario,we formulate a joint participant selection and learning optimization problem in a shared edge cloud.This joint optimization aims to determine FL participants and the learning schedule for each FL model such that the total training cost of all FL models in the edge cloud is minimized.We propose a multi-stage optimization framework by decoupling the original problem into two or three subproblems that can be solved respectively and iteratively.Extensive evaluation has been conducted with realworld FL datasets and models.The results have shown that our proposed algorithms can reduce the total cost efficiently compared with prior algorithms.