【Author】
Rehman, Muhammad Habib Ur; Salah, Khaled; Damiani, Ernesto; Svetinovic, Davor
【Source】IEEE INFOCOM 2020 - IEEE CONFERENCE ON COMPUTER COMMUNICATIONS WORKSHOPS (INFOCOM WKSHPS)
【Abstract】Federated learning (FL) is the collaborative machine learning (ML) technique whereby the devices collectively train and update a shared ML model while preserving their personal datasets. FL systems solve the problems of communication efficiency, bandwidth-optimization, and privacy-preservation. Despite the potential benefits of FL, one centralized shared ML model across all the devices produce coarse-grained predictions which, in essence, are not required in many application areas involving personalized prediction services. In this paper, we present a novel concept of fine-grained FL to decentralize the shared ML models on the edge servers. We then present a formal extended definition of fine-grained FL process in mobile edge computing systems. In addition, we define the core requirements of fine-grained FL systems including personalization, decentralization, fine-grained FL, incentive mechanisms, trust, activity monitoring, heterogeneity and context-awareness, model synchronization, and communication and bandwidth-efficiency. Moreover, we present the concept of blockchain-based reputation-aware fine-grained FL in order to ensure trustworthy collaborative training in mobile edge computing systems. Finally, we perform the qualitative comparison of proposed approach with state-of-the-art related work and found some promising initial results.
【Keywords】blockchain; machine learning; federated learning; mobile edge computing; reputation; trust
【摘要】联邦学习 (FL) 是一种协作机器学习 (ML) 技术，设备通过该技术共同训练和更新共享的 ML 模型，同时保留其个人数据集。 FL 系统解决了通信效率、带宽优化和隐私保护的问题。尽管 FL 有潜在的好处，但一个跨所有设备的集中共享 ML 模型会产生粗粒度的预测，这在涉及个性化预测服务的许多应用领域中本质上是不需要的。在本文中，我们提出了细粒度 FL 的新概念，以分散边缘服务器上的共享 ML 模型。然后，我们提出了移动边缘计算系统中细粒度 FL 过程的正式扩展定义。此外，我们定义了细粒度 FL 系统的核心要求，包括个性化、去中心化、细粒度 FL、激励机制、信任、活动监控、异质性和上下文感知、模型同步以及通信和带宽效率。此外，我们提出了基于区块链的信誉感知细粒度 FL 的概念，以确保在移动边缘计算系统中进行可信的协作训练。最后，我们对提出的方法与最先进的相关工作进行了定性比较，并发现了一些有希望的初步结果。
【关键词】区块链；机器学习；联邦学习；移动边缘计算；名声;相信
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