Performance Observations from Split Federated Learning on Heterogeneous Devices & Networks
Bibliographic record
Abstract
Split Federated Learning (SFL) combines the scalability of Federated Learning (FL) with the computational efficiency of Split Learning (SL), making it a promising paradigm for distributed machine learning in heterogeneous environments. Existing studies have explored theoretical convergence and split layer selection, however, the practical implications of split layer selection with client heterogeneity and network variability remain under explored. This paper investigates the impact of heterogeneous client resources, network conditions, and split layer configurations on the performance of SFL. Using the CIFAR-10 dataset, we implement a SFL model with heterogeneous clients. Experiments include diverse configurations, such as uniform and varying split layers among clients. We analyze training time, idle and active times, and resource utilization to understand the effects of heterogeneity. Our observations show that strategic split layer configurations tailored to heterogeneous environments can improve training efficiency by 26.7%. Additionally, we observed the effects that heterogeneous networks have on SFL and how to compensate for it, offering valuable insights for deploying SFL in real-world computing 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".