A Unifying Framework for Federated Learning
Why this work is in the frame
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Bibliographic record
Abstract
There have been multiple federated learning (FL) algorithms proposed in the FL community during the recent years. However, a thorough comparison of these algorithms has not been done, and our understanding of the theory of FL is still limited. The lack of a unifying view in practice has also led to the reinvention of the same algorithms under different names. Motivated by this gap, we develop a unifying scheme for FL and demonstrate that many of the algorithms that exist in the FL literature are special cases of this scheme. The unification allows us to get a deeper understanding of different FL algorithms, to compare them easier, to improve the previous results for their convergence analysis and to find new FL algorithms. In particular, we demonstrate the important role that step size plays in the convergence of FL algorithms. Further, based on our unifying scheme, we propose an efficient and economic method for accelerating FL algorithms. This streamlined acceleration method does not incur any communication overheads. We evaluate our findings by performing extensive experiments on both nonconvex and convex problems.
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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 | 0.000 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.000 | 0.001 |
| 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 it