Heterogeneous Federated Learning: State-of-the-art and Research Challenges

203 indexed citations

Abstract

loading...

About

This paper, published in 2023, received 203 indexed citations. Written by Mang Ye, Bo Du, Pong C. Yuen and Dacheng Tao covering the research area of Computer Science Applications and Artificial Intelligence. It is primarily cited by scholars working on Artificial Intelligence (169 citations), Computer Networks and Communications (32 citations) and Information Systems (28 citations). Published in ACM Computing Surveys.

In The Last Decade

doi.org/10.1145/3625558 →

Countries where authors are citing Heterogeneous Federated Learning: State-of-the-art and Research Challenges

Specialization
Citations

This map shows the geographic impact of Heterogeneous Federated Learning: State-of-the-art and Research Challenges. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Heterogeneous Federated Learning: State-of-the-art and Research Challenges with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Heterogeneous Federated Learning: State-of-the-art and Research Challenges more than expected).

Fields of papers citing Heterogeneous Federated Learning: State-of-the-art and Research Challenges

Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of Heterogeneous Federated Learning: State-of-the-art and Research Challenges. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Heterogeneous Federated Learning: State-of-the-art and Research Challenges.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

This paper is also available at doi.org/10.1145/3625558.

Explore hit-papers with similar magnitude of impact

Rankless by CCL
2026