An Introduction to Federated Computation
Summary: Federated computation: privacy-preserving distributed data processing with local data and only shared summaries. Overview of models, algorithms, security guarantees, and practical system issues, highlighting open problems for data management beyond federated learning. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Akash Bharadwaj (Meta)
- 2. Graham Cormode (Meta)
BibTeX Citation
@inproceedings{bharadwaj_sigmod22,
title = {{An Introduction to Federated Computation}},
author = {Bharadwaj, Akash and Cormode, Graham},
series = {{SIGMOD} '22},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3514221.3522561},
url = {https://dl.acm.org/doi/10.1145/3514221.3522561},
year = {2022}
}Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,857 | RAGraph: A Region-Aware Framework for Geo-Distributed Graph Processing | 2024 | VLDB | 5.7525746e-05 |
| 9,473 | FEAST: A Communication-efficient Federated Feature Selection Framework for Relational Data | 2023 | SIGMOD | 5.2634238e-05 |
| 10,869 | Federated Data Distribution Shift Estimation | 2025 | VLDB | 5.093636e-05 |
| 11,375 | Applications of Sketching and Pathways to Impact | 2023 | PODS | 5.093636e-05 |
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 0 of 0 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 6,406 | Skellam Mixture Mechanism: a Novel Approach to Federated Learning with Differential Privacy | 2022 | VLDB |
| 2 | 10,869 | Federated Data Distribution Shift Estimation | 2025 | VLDB |
| 3 | 3,237 | BlindFL: Vertical Federated Machine Learning without Peeking into Your Data | 2022 | SIGMOD |
| 4 | 1,249 | Privacy Preserving Vertical Federated Learning for Tree-based Models | 2020 | VLDB |
| 5 | 11,462 | Federated Calibration and Evaluation of Binary Classifiers | 2023 | VLDB |
| 6 | 5,588 | Federated Matrix Factorization with Privacy Guarantee | 2022 | VLDB |
| 7 | 8,045 | Contributions Estimation in Federated Learning: A Comprehensive Experimental Evaluation | 2024 | VLDB |
| 8 | 12,347 | Secure and Privacy-Preserving Data Services in the Cloud: A Data Centric View | 2012 | VLDB |
| 9 | 4,446 | Projected Federated Averaging with Heterogeneous Differential Privacy | 2022 | VLDB |
| 10 | 8,917 | A Blockchain System for Clustered Federated Learning with Peer-to-Peer Knowledge Transfer | 2024 | VLDB |