Federated Learning: Challenges, Methods, and Future Directions
Authors: Tian Li, Anit Kumar Sahu, Ameet Talwalkar, Virginia Smith
Abstract: Federated learning involves training statistical models over remote devices or siloed data centers, such as mobile phones or hospitals, while keeping data localized. Training in heterogeneous and potentially massive networks introduces novel challenges that require a fundamental departure from standard approaches for large-scale machine learning, distributed optimization, and privacy-preserving data analysis. In this article, we discuss the unique characteristics and challenges of federated learning, provide a broad overview of current approaches, and outline several directions of future work that are relevant to a wide range of research communities.
Tools for this paper
Ask questions about this paper to our AI assistant
You can also chat with multiple papers at once here.
⚠The license of the paper does not allow us to build upon its content and the AI assistant only knows about the paper metadata rather than the full article.
The previous summary was created more than a year ago and can be re-run (if necessary) by clicking on the Run button below.
⚠The license of this specific paper does not allow us to build upon its content and the summarizing tools will be run using the paper metadata rather than the full article. However, it still does a good job, and you can also try our tools on papers with more open licenses.
Similar papers summarized with our AI tools
Navigate through even more similar papers through a
tree representationLook for similar papers (in beta version)
We compare the title and abstract with papers indexed in our database. For arXiv papers, we search within the same category; for DOI and other sources, we search across sources and subjects. You can rerun the search as our database grows.
Disclaimer: The AI-based summarization tool and virtual assistant provided on this website may not always provide accurate and complete summaries or responses. We encourage you to carefully review and evaluate the generated content to ensure its quality and relevance to your needs.