Pack: Towards Communication-Efficient Homomorphic Encryption in Federated Learning
Bibliographic record
Abstract
Federated learning allows multiple clients to collaboratively train a shared model without sharing local private data. It is regarded as privacy-preserving since only model updates are communicated. Unfortunately, it has been shown in the recent literature that, model updates transmitted by participating clients can be used by a malicious server in gradient leakage attacks to obtain private training data. To prevent such potential leakage from occurring, it has widely been acknowledged that homomorphic encryption can be used to encrypt these model updates before sending them to the server, which performs computations directly on encrypted data. Although homomorphic encryption has a strong guarantee on privacy, its practical use increases communication overhead by around 17×, even with its most efficient implementation, called CKKS. In this paper, we present Pack, a novel communication-efficient mechanism over CKKS, designed specifically to reduce the communication overhead by a substantial margin. In addition, we propose new error correction and weight filtering mechanisms in Pack to improve the accuracy of the trained model. Compared to vanilla CKKS, Pack reduces the communication overhead by 3.1×, while increasing the accuracy by 5.5% and 2.5% under the i.i.d. and non-i.i.d. settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".