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The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning
Source: proceedings.mlr.press
Article summary:
1. The paper characterizes the fundamental communication cost required to obtain the best accuracy achievable under ε central DP when using secure aggregation (SecAgg).
2. The results show that O~\lpmin(n2ε2,d)\rp bits per client are both sufficient and necessary for this task.
3. Empirically, the proposed scheme was evaluated on real-world federated learning tasks and found to reduce communication cost to under 1.78 bits per parameter without decreasing test-time performance.
Article analysis:
The article is well written and provides a thorough analysis of the problem of training a d dimensional model with distributed differential privacy (DP) where secure aggregation (SecAgg) is used to ensure that the server only sees the noisy sum of n model updates in every training round. The authors provide a detailed characterization of the fundamental communication cost required to obtain the best accuracy achievable under ε central DP, as well as an empirical evaluation of their proposed scheme on real-world federated learning tasks.
The article does not appear to be biased or one-sided in its reporting, as it presents both theoretical and empirical evidence for its claims. Furthermore, all claims are supported by evidence from experiments or other sources, and counterarguments are explored where appropriate. There is no promotional content present in the article, nor any partiality towards any particular point of view or solution. Possible risks associated with using SecAgg are noted throughout the paper, and both sides of an argument are presented equally when relevant.
In conclusion, this article appears to be trustworthy and reliable in its reporting and analysis of secure aggregation in differentially private federated learning.