AddShare+: Efficient Selective Additive Secret Sharing Approach for Private Federated Learning
Bibliographic record
Abstract
Federated Learning (FL) enables collaborative training of Machine Learning (ML) models while maintaining user data privacy. However, leaked model updates can reveal private training data. Existing solutions using additive secret sharing introduce intermediary servers, increasing complexity and communication overhead, and often lack privacy guarantees. We propose AddShare+, which enhances efficiency and scalability by creating additive shares for a subset of model weight parameters and using the Elliptic Curve Integrated Encryption Scheme (ECIES) for faster, lighter model encryption. By sampling and splitting a percentage of local weight parameters, AddShare+ reduces computation and communication costs while maintaining model accuracy. We implemented and evaluated AddShare+ on multiple datasets, comparing it with baseline approaches including FedAvg, SCOTCH, FedShare, and AddShare. Results demonstrate that AddShare+ maintains accuracy while significantly reducing running time per round. Notably, sharing as low as 25% of model weights decreases bandwidth demands by over 5x while preserving accuracy within 0.05 % of the full model. Our empirical results demonstrate significant reductions in running time per round with strong privacy guarantees, highlighting the potential of lightweight partial sharing solutions for privacy-preserving FL in resource-constrained environments, paving the way for more efficient and secure collaborative learning systems.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".