Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/8813
Title: A Survey on Trusted Distributed Artificial Intelligence
Authors: Agca M.A.
Faye S.
Khadraoui D.
Keywords: Distributed systems
Software defined networking (SDN)
Trusted AI
Trusted execution environment (TEE)
Adaptive control systems
Artificial intelligence
Distributed computer systems
Network security
Peer to peer networks
Surveys
Distributed Artificial Intelligence
Distributed systems
Peer-to-peer computing
Security
Software defined networking
Software-defined networkings
Trusted artificial intelligence
Trusted execution environment
Trusted execution environments
Software defined networking
Issue Date: 2022
Publisher: Institute of Electrical and Electronics Engineers Inc.
Abstract: Emerging Artificial Intelligence (AI) systems are revolutionizing computing and data processing approaches with their strong impact on society. Data is processed with automated labelling pipelines rather than providing it as input to the system. The innovative nature increases the overall performance of monitoring/detection/reaction mechanisms for efficient system resource management. However, due to hardware-driven design limitations, networking and trust mechanisms are not flexible and adaptive enough to be able to interact and control the resources dynamically. Novel adaptive software-driven design approaches can enable us to build growing intelligent mechanisms with software-defined networking (SDN) features by virtualizing network functionalities with maximized features. These challenges and critical feature sets have been identified and introduced into this survey with their scientific background for AI systems and growing intelligent mechanisms. Furthermore, obstacles and research challenges between 1950-2021 are explored and discussed with a focus on recent years. The challenges are categorized according to three defined architectural perspectives (central, decentral/autonomous, distributed/hybrid) for emerging trusted distributed AI mechanisms. Therefore, resiliency and robustness can be assured in a dynamic context with an end-to-end Trusted Execution Environment (TEE) for growing intelligent mechanisms and systems. Furthermore, as presented in the paper, the trust measurement, quantification, and justification methodologies on top of Trusted Distributed AI (TDAI) can be applied in emerging distributed systems and their underlying diverse application domains, which will be explored and experimented in our future related works. © 2013 IEEE.
URI: https://doi.org/10.1109/ACCESS.2022.3176385
https://hdl.handle.net/20.500.11851/8813
ISSN: 2169-3536
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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