Summary of Federated Learning in Practice: Reflections and Projections, by Katharine Daly et al.
Federated Learning in Practice: Reflections and Projections
by Katharine Daly, Hubert Eichner, Peter Kairouz, H. Brendan McMahan, Daniel Ramage, Zheng Xu
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
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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 Federated Learning (FL) is a machine learning technique that enables multiple entities to collaboratively learn a shared model without exchanging their local data. Recent advances in FL have achieved substantial progress, scaling to millions of devices across various learning domains while offering meaningful differential privacy (DP) guarantees. Production systems from organizations like Google, Apple, and Meta demonstrate the real-world applicability of FL. However, key challenges remain, including verifying server-side DP guarantees and coordinating training across heterogeneous devices. To address these limitations, we propose a redefined FL framework that prioritizes privacy principles rather than rigid definitions. This framework leverages trusted execution environments and open-source ecosystems to facilitate future advancements in FL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated Learning is a way for many devices or organizations to work together on machine learning projects without sharing their own data. It’s like a team effort where everyone contributes, but nobody shares what they know. This technique has been successful and is used by big companies like Google and Apple. However, there are still some problems that need to be solved, such as making sure the servers are private and making it easy for devices with different abilities to work together. To fix these issues, we’re proposing a new way of doing Federated Learning that focuses on keeping things private and using special tools to make it easier. |
Keywords
» Artificial intelligence » Federated learning » Machine learning
