Summary of Federated Learning For Collaborative Inference Systems: the Case Of Early Exit Networks, by Caelin Kaplan et al.
Federated Learning for Collaborative Inference Systems: The Case of Early Exit Networks
by Caelin Kaplan, Angelo Rodio, Tareq Si Salem, Chuan Xu, Giovanni Neglia
First submitted to arxiv on: 7 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)
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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 A novel Federated Learning approach is proposed to optimize Cooperative Inference Systems (CIS), which enable smaller devices to offload inference tasks to more capable devices. The framework addresses the performance trade-off between local and cloud-based models by accounting for operational dynamics, particularly heterogeneous serving rates among clients. This paper presents a rigorous theoretical guarantee and outperforms state-of-the-art training algorithms in scenarios with uneven client request rates or data availability. The proposed approach is designed specifically for CISs that utilize hierarchical models like Deep Neural Networks (DNNs) with strategies like early exits or ordered dropout. Key techniques include Federated Learning, Cooperative Inference Systems, and heterogeneous serving rates. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a world where small devices can work together to do tasks faster and more efficiently. This paper talks about how we can make this happen by letting smaller devices share some of their work with more powerful devices. It’s like having a team of experts working together to get things done. The big challenge is that each device has different abilities, so we need to find a way to make sure they all work well together. This paper proposes a new way to do just that, using something called Federated Learning. It’s designed specifically for these kinds of teams, where devices have different strengths and weaknesses. |
Keywords
» Artificial intelligence » Dropout » Federated learning » Inference
