AutoFL: Cross-Layer Trusted Systems for Heterogeneous Federated Learning at Scale
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This project will develop algorithms and tools to provide a generic FL framework to deploy trustworthy and energy-efficient ML applications onto heterogeneous edge devices. It uses a cross-layer approach to utilize the full potential of the FL processing flow from communication scheduling to hardware deployment. To this aim, we will (i) develop adaptive scheduling mechanisms that guarantee the system predictability; (ii) design scalable and energy-efficient heterogeneous machine learning (HML) models customized for each FL node individually with guaranteeing user requirements; (iii) design trustworthy FLS that are resistant to different types of cyber-attacks as well as noisy environments; and (iv) facilitate fast deployment and effective maintenance of HML models.
A Systematic Literature Review on Hardware Reliability Assessment Methods for Deep Neural Networks (Jan 2024) Mohammad Ahmadilivani , Mahdi Taheri , Jaan Raik , Masoud Daneshtalab, Maksim Jenihhin ACM Computing Surveys (CSUR)
