Summary of Adaptive Decentralized Federated Learning in Energy and Latency Constrained Wireless Networks, by Zhigang Yan and Dong Li
Adaptive Decentralized Federated Learning in Energy and Latency Constrained Wireless Networks
by Zhigang Yan, Dong Li
First submitted to arxiv on: 29 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY)
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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 Decentralized Federated Learning (DFL) approach is presented to alleviate the communication overhead and single point of failure in traditional Federated Learning. The problem of efficiently leveraging limited device resources to enhance model performance is investigated, considering energy and latency constraints. A mathematical formulation minimizes the loss function of DFL while optimizing the number of local training rounds across diverse devices with varying resource budgets. Convergence analysis reveals the impact of local training rounds on model performance, leading to closed-form solutions for optimized rounds in different devices. To minimize energy consumption, graph-based aggregation schemes are modified and applied to various communication scenarios. A joint DFL framework is proposed, which combines optimized local training rounds and energy-saving aggregation. Simulation results demonstrate improved performance and reduced energy consumption compared to traditional methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists are working on a way to make computers learn together without having to send lots of information between them. They want to find the best way for devices with different resources (like power and processing speed) to work together to improve their learning. To do this, they’re using math to figure out how many times each device should learn on its own before sharing what it’s learned with others. They’re also looking at ways to make sure the process doesn’t use too much energy or take too long. The goal is to create a new way of doing things that will help devices work together more efficiently and effectively. |
Keywords
* Artificial intelligence * Federated learning * Loss function
