Federated Learning for Computer Vision
Yassine Himeur Iraklis Varlamis Hamza Kheddar Abbes Amira Shadi Atalla Yashbir Singh Faycal Bensaali Wathiq Mansoor
Computer Vision (CV) is playing a significant role in transforming society by
utilizing machine learning (ML) tools for a wide range of tasks. However, the
need for large-scale datasets to train ML models creates challenges for
centralized ML algorithms. The massive computation loads required for
processing and the potential privacy risks associated with storing and
processing data on central cloud servers put these algorithms under severe
strain. To address these issues, federated learning (FL) has emerged as a
promising solution, allowing privacy preservation by training models locally
and exchanging them to improve overall performance. Additionally, the
computational load is distributed across multiple clients, reducing the burden
on central servers. This paper presents, to the best of the authors' knowledge,
the first review discussing recent advancements of FL in CV applications,
comparing them to conventional centralized training paradigms. It provides an
overview of current FL applications in various CV tasks, emphasizing the
advantages of FL and the challenges of implementing it in CV. To facilitate
this, the paper proposes a taxonomy of FL techniques in CV, outlining their
applications and security threats. It also discusses privacy concerns related
to implementing blockchain in FL schemes for CV tasks and summarizes existing
privacy preservation methods. Moving on, the paper identifies open research
challenges and potential future research directions to further exploit the
potential of FL and blockchain in CV applications.