FLAC: Federated Learning with Autoencoder Compression and Convergence Guarantee
Bibliographic record
Abstract
Federated Learning (FL) is considered the key approach for privacy-preserving, distributed machine learning (ML) systems. However, due to the transmission of large ML models from users to the server in each iteration of FL, communication on resource-constrained networks is currently a fundamental bottleneck in FL, restricting the ML model complex-ity and user participation. One of the notable trends to reduce the communication cost of FL systems is gradient compression, in which techniques in the form of sparsification or quantization are utilized. However, these methods are pre-fixed and do not capture the redundant, correlated information across parameters of the ML models, user devices' data, and iterations of FL. Further, these methods do not fully take advantage of the error-correcting capability of the FL process. In this paper, we propose the Federated Learning with Autoencoder Compression (FLAC) approach that utilizes the redundant information and error-correcting capability of FL to compress user devices' models for uplink transmission. FLAC trains an autoencoder to encode and decode users' models at the server in the Training State, and then, sends the autoencoder to user devices for compressing local models for future iterations during the Compression State. To guarantee the convergence of the FL, FLAC dynamically controls the autoencoder error by switching between the Training State and Compression State to adjust its autoencoder and its compression rate based on the error tolerance of the FL system. We theoretically prove that FLAC converges for FL systems with strongly convex ML models and non-i.i.d. data distribution. Our extensive experimental results'over three datasets with different network architectures show that FLAC can achieve compression rates ranging from 83x to 875x while staying near 7 percent of the accuracy of the non-compressed FL 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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".