Summary of Causal Influence in Federated Edge Inference, by Mert Kayaalp et al.
Causal Influence in Federated Edge Inference
by Mert Kayaalp, Yunus Inan, Visa Koivunen, Ali H. Sayed
First submitted to arxiv on: 2 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Multiagent Systems (cs.MA); Signal Processing (eess.SP); 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 The paper investigates how heterogeneous agents with connectivity perform inference using unlabeled streaming data in the presence of uncertainty. Agents cooperate by exchanging their local inferences through a fusion center, and we evaluate their influence on the overall decision using a causal framework to distinguish actual influence from correlation. We explore various scenarios reflecting different agent participation patterns and fusion center policies, deriving expressions to quantify the causal impact of each agent. Our theoretical results are validated through numerical simulations and a real-world application in multi-camera crowd counting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists study how different agents work together to make decisions when they have incomplete information. They use a special way of thinking called “causal framework” to figure out what each agent’s contribution is really worth. The researchers test their ideas with computer simulations and also apply it to a real-world problem like counting people in crowds using multiple cameras. |
Keywords
» Artificial intelligence » Inference
