Computation Trade-offs
Computation Trade-Offs is a framework in the study of communication overhead that systematically characterizes the balance between computational complexity and communication cost in distributed systems, distinguishing itself by formalizing how increased computation can reduce message exchange while optimizing overall performance.
52
Publications
4.4K
Citations
134
Authors
66
Institutions
Publications per year
2017–2026
28
Parents
52
Client-Edge-Cloud Hierarchical Federated Learning
Lumin Liu, Jun Zhang, Shenghui Song et al. · 2020 · 886 citations
Video Classification With Channel-Separated Convolutional Networks
Du Tran, Heng Wang, Matt Feiszli et al. · 2019 · 628 citations
1–5 of 52
134
5
h-index
University of Southern California, United States
5 publications · 764 citations
5
h-index
5
h-index
4
h-index
4
h-index
1–5 of 134
66
University of Utah
Salt Lake City, United States
4 h-index
12 publications
330 citations
University of Southern California
Los Angeles, United States
4 h-index
10 publications
2K citations

Hong Kong Polytechnic University
Hong Kong, Hong Kong
3 h-index
4 publications
1.2K citations
Meta (Israel)
Tel Aviv, Israel
2 h-index
5 publications
2.5K citations
University of Washington
Seattle, United States
2 h-index
5 publications
81 citations
Google (United States)
Mountain View, United States
2 h-index
8 publications
254 citations
The University of Texas at Austin
Austin, United States
2 h-index
2 publications
28 citations

University of Toronto
Toronto, Canada
2 h-index
2 publications
32 citations
1–8 of 66
Repository · 10 publications · 252 citations
10
h-index
3 publications · 70 citations
3
h-index
2 publications · 89 citations
2
h-index
2 publications · 115 citations
2
h-index
PVLDB · 1 publications · 135 citations
1
h-index
1–5