AI at the Edge: Blockchain-Empowered Secure Multiparty Learning With Heterogeneous Models
Autor: | Lixing Yu, Pan Li, Yifan Guo, Xufei Wang, Tianxi Ji, Qianlong Wang |
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Rok vydání: | 2020 |
Předmět: |
Computer Networks and Communications
Computer science business.industry Distributed computing Big data 020206 networking & telecommunications 02 engineering and technology 010501 environmental sciences 01 natural sciences Computer Science Applications Data modeling Hardware and Architecture Server Signal Processing Scalability 0202 electrical engineering electronic engineering information engineering Enhanced Data Rates for GSM Evolution Resilience (network) business Edge computing 0105 earth and related environmental sciences Information Systems |
Zdroj: | IEEE Internet of Things Journal. 7:9600-9610 |
ISSN: | 2372-2541 |
Popis: | Edge computing, an emerging computing paradigm pushing data computing and storing to network edges, enables many applications that require high computing complexity, scalability, and security. In the big data era, one of the most critical applications is multiparty learning or federated learning, which allows different parties to collaborate with each other to obtain better learning models without sharing their own data. However, there are several main concerns about the current multiparty learning systems. First, most existing systems are distributed and need a central server to coordinate the learning process. However, such a central server can easily become a single point of failure and may not be trustworthy. Second, although quite a few schemes have been proposed to study Byzantine attacks, a very common and challenging kind of attack in distributed systems, they generally consider the scenario of learning a global model. However, in fact, all parties in multiparty learning usually have their own local models. The learning methods and security issues, in this case, are not fully explored. In this article, we propose a novel blockchain-empowered decentralized secure multiparty learning system with heterogeneous local models called BEMA. Particularly, we consider two types of Byzantine attacks, and carefully design “off-chain sample mining” and “on-chain mining ” schemes to protect the security of the proposed system. We theoretically prove the system performance bound and resilience under Byzantine attacks. The simulation results show that the proposed system obtains comparable performance with that of conventional distributed systems, and bounded performance in the case of Byzantine attacks. |
Databáze: | OpenAIRE |
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