Summary of Trustworthy Distributed Ai Systems: Robustness, Privacy, and Governance, by Wenqi Wei and Ling Liu
Trustworthy Distributed AI Systems: Robustness, Privacy, and Governance
by Wenqi Wei, Ling Liu
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Distributed, Parallel, and Cluster Computing (cs.DC)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Distributed learning in emerging Artificial Intelligence (AI) systems has transformed big data processing capabilities, but recent studies highlight security, privacy, and fairness concerns. This paper reviews techniques, algorithms, and theoretical foundations for trustworthy distributed AI, guaranteeing robustness, protecting privacy, and ensuring fairness through distributed learning. The authors analyze vulnerabilities in AI algorithms across various architectures, highlighting the need for countermeasures to ensure trustworthy distributed AI. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Distributed AI is changing how we process big data! But it also raises concerns about security, privacy, and fairness. This paper explores ways to make distributed AI more trustworthy, so our data and models are safe and fair. The authors look at the problems that can happen with AI algorithms and suggest solutions to fix them. |
