Causal Influence in Federated Edge Inference
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929687362535424 |
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| author | Kayaalp, Mert Inan, Yunus Koivunen, Visa Sayed, Ali H. |
| author_facet | Kayaalp, Mert Inan, Yunus Koivunen, Visa Sayed, Ali H. |
| contents | In this paper, we consider a setting where heterogeneous agents with connectivity are performing inference using unlabeled streaming data. Observed data are only partially informative about the target variable of interest. In order to overcome the uncertainty, agents cooperate with each other by exchanging their local inferences with and through a fusion center. To evaluate how each agent influences the overall decision, we adopt a causal framework in order to distinguish the actual influence of agents from mere correlations within the decision-making process. Various scenarios reflecting different agent participation patterns and fusion center policies are investigated. We derive expressions to quantify the causal impact of each agent on the joint decision, which could be beneficial for anticipating and addressing atypical scenarios, such as adversarial attacks or system malfunctions. We validate our theoretical results with numerical simulations and a real-world application of multi-camera crowd counting. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2405_01260 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Causal Influence in Federated Edge Inference Kayaalp, Mert Inan, Yunus Koivunen, Visa Sayed, Ali H. Machine Learning Multiagent Systems Systems and Control Signal Processing In this paper, we consider a setting where heterogeneous agents with connectivity are performing inference using unlabeled streaming data. Observed data are only partially informative about the target variable of interest. In order to overcome the uncertainty, agents cooperate with each other by exchanging their local inferences with and through a fusion center. To evaluate how each agent influences the overall decision, we adopt a causal framework in order to distinguish the actual influence of agents from mere correlations within the decision-making process. Various scenarios reflecting different agent participation patterns and fusion center policies are investigated. We derive expressions to quantify the causal impact of each agent on the joint decision, which could be beneficial for anticipating and addressing atypical scenarios, such as adversarial attacks or system malfunctions. We validate our theoretical results with numerical simulations and a real-world application of multi-camera crowd counting. |
| title | Causal Influence in Federated Edge Inference |
| topic | Machine Learning Multiagent Systems Systems and Control Signal Processing |
| url | https://arxiv.org/abs/2405.01260 |