Summary of Federated Analytics in Practice: Engineering For Privacy, Scalability and Practicality, by Harish Srinivas et al.
Federated Analytics in Practice: Engineering for Privacy, Scalability and Practicality
by Harish Srinivas, Graham Cormode, Mehrdad Honarkhah, Samuel Lurye, Jonathan Hehir, Lunwen He, George Hong, Ahmed Magdy, Dzmitry Huba, Kaikai Wang, Shen Guo, Shoubhik Bhattacharya
First submitted to arxiv on: 3 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 presents a distributed computation paradigm called Cross-device Federated Analytics (FA) that enables analysts to derive insights from data held locally on users’ devices while ensuring high standards of data protection. FA combines on-device computations with privacy and security measures, transmitting only minimal data off-device. However, existing FA systems are limited by compromised accuracy, lack of flexibility for data analytics, and inability to scale effectively. To overcome these limitations, the authors propose a system that leverages trusted execution environments (TEEs) and optimizes on-device computing resources to facilitate federated data processing across large fleets of devices while ensuring robust privacy safeguards. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Cross-device Federated Analytics is a new way to analyze data from many different devices without having to send all the data to one place. This makes it more private because only a little bit of data needs to be shared. Right now, this technology isn’t perfect because it’s not very good at getting accurate results or doing complex analysis. It also can’t handle big groups of devices well. The researchers in this paper want to make it better by using special computer environments and making the most of each device’s computing power. This will let them do more complex analytics and process data from many devices quickly and securely. |
