Leveraging The Edge-to-Cloud Continuum for Scalable Machine Learning on Decentralized Data
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Abstract (arXiv)
With mobile, IoT and sensor devices becoming pervasive in our life and recent advances in Edge Computational Intelligence (e.g., Edge AI/ML), it became evident that the traditional methods for training AI/ML models are becoming obsolete, especially with the growing concerns over privacy and security. This work tries to highlight the key challenges that prohibit Edge AI/ML from seeing wide-range adoption in different sectors, especially for large-scale scenarios. Therefore, we focus on the main challenges acting as adoption barriers for the existing methods and propose a design with a drastic shift from the current ill-suited approaches. The new design is envisioned to be model-centric in which the trained models are treated as a commodity driving the exchange dynamics of collaborative learning in decentralized settings. It is expected that this design will provide a decentralized framework for efficient collaborative learning at scale.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 16 of its 32 references that are in the map. First posted on arXiv 19 Jun 2023.
Builds on
16 of 32 references.
References · 16
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4 more
- 2021Resource-Efficient Federated Learningcited by 26
- 2020Which Model to Transfer? Finding the Needle in the Growing Haystackcited by 21
- 2021Collaboration Equilibrium in Federated Learningcited by 16
- 2022Towards Energy-Aware Federated Learning on Battery-Powered Clientscited by 11
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