Summary of Enhancing Equitable Access to Ai in Housing and Homelessness System Of Care Through Federated Learning, by Musa Taib et al.
Enhancing Equitable Access to AI in Housing and Homelessness System of Care through Federated Learning
by Musa Taib, Jiajun Wu, Steve Drew, Geoffrey G. Messier
First submitted to arxiv on: 14 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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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 This paper introduces a Federated Learning (FL) approach to address the gap in information technology platforms within Housing and Homelessness Systems of Care (HHSC). Specifically, it enables smaller agencies to train a predictive model collaboratively without sharing their sensitive data. By preserving the privacy of individuals, the FL approach provides equitable access to quality AI for all agencies, improving resource allocation decision-making. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this system, people experiencing homelessness are connected to supportive housing through a top priority. However, information technology platforms differ between agencies, making it hard to share data. This paper solves this problem by introducing Federated Learning (FL) to help smaller agencies train AI models without sharing their sensitive data. It works by preserving privacy and providing equal access to quality AI for all agencies. |
Keywords
» Artificial intelligence » Federated learning
