Summary of On-demand Model and Client Deployment in Federated Learning with Deep Reinforcement Learning, by Mario Chahoud et al.
On-Demand Model and Client Deployment in Federated Learning with Deep Reinforcement Learning
by Mario Chahoud, Hani Sami, Azzam Mourad, Hadi Otrok, Jamal Bentahar, Mohsen Guizani
First submitted to arxiv on: 12 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed On-Demand solution for Federated Learning (FL) tackles the challenge of restricted user participation by expanding client access and diversifying data. By deploying new clients using Docker Containers on-the-fly, the solution addresses the issue of certain devices becoming inaccessible as FL clients. The architecture employs Deep Reinforcement Learning (DRL), utilizing a Markov Decision Process (MDP) framework with a Master Learner and a Joiner Learner to target client availability and selection. Simulated tests demonstrate the ability of the architecture to adjust to environmental changes and respond to On-Demand requests, improving client availability, capability, accuracy, and learning efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists developed a new way to learn from different devices that are connected to the internet. They wanted to make sure that everyone can participate in the learning process, even if their device is not always available or strong. To solve this problem, they created a special system that can add new devices as needed and choose which devices should be used for learning. This system uses a special type of artificial intelligence called Deep Reinforcement Learning (DRL) to make decisions about which devices are best suited for the task. The results show that this system is very good at adapting to changing situations and making sure that everyone can participate in the learning process. |
Keywords
» Artificial intelligence » Federated learning » Reinforcement learning
