Summary of Strategic Federated Learning: Application to Smart Meter Data Clustering, by Hassan Mohamad et al.
Strategic Federated Learning: Application to Smart Meter Data Clustering
by Hassan Mohamad, Chao Zhang, Samson Lasaulce, Vineeth S Varma, Mérouane Debbah, Mounir Ghogho
First submitted to arxiv on: 5 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Science and Game Theory (cs.GT)
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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 paper introduces a novel federated learning (FL) framework that incorporates the concept of model information (MI) into the decision-making process. In conventional FL, clients share their trained models with a fusion center (FC), without considering how this information is used by the FC or other clients. This new approach allows the FC to use an aggregate version of the MI to make decisions that affect client utility functions. Clients can only use the reported MI to maximize their own utility, and may have individual interests in adding strategic noise to the model. The framework is specialized for clustering, where noisy cluster representative information is reported. Numerical analysis shows that clients can increase their utility by adding noise to the reported model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning is a way for many devices to work together to improve their models without sharing all their data. In this paper, researchers introduce a new approach that makes better decisions by using information from each device’s model. The main idea is that the devices can’t choose what decisions are made, but they can use the information reported back to them to make good choices for themselves. This can lead to interesting situations where one device might want to add “noise” or fake data to their model to get a better outcome. The researchers show this works by using real data from a power company in Australia and demonstrate that devices can actually increase their own benefits by adding noise. |
Keywords
» Artificial intelligence » Clustering » Federated learning
