Privacy-Preserving Model Aggregation for Asynchronous Federated Learning
Bibliographic record
Abstract
We present a novel privacy-preserving model aggregation for asynchronous federated learning, named PPA-AFL that removes the restriction of synchronous aggregation of local model updates in federated learning, while enabling the protection of the local model updates against the server. In PPA-AFL, clients can proactive decide when to engage in the training process, and sends local model updates to the server when the updates are available. Thus, it is not necessary to keep synchronicity with other clients. To safeguard client updates and facilitate local model aggregation, we employ Paillier encryption for local update encryption and support homomorphic aggregation. Furthermore, secret sharing is utilized to enable the sharing of decryption keys and facilitate privacy-preserving asynchronous aggregation. As a result, the server remains unable to gain any information about the local updates while asynchronously aggregating to produce the global model. We demonstrate the efficacy of our proposed PPA-AFL framework through comprehensive complexity analysis and extensive experiments on a prototype implementation, high-lighting its potential for practical adoption in privacy-sensitive asynchronous federated learning scenarios.1
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".