Joint Computational Resource Allocation and Layer Partitioning for Federated Learning
Bibliographic record
Abstract
Despite its popularity, federated learning (FL) in heterogeneous networks faces two critical challenges, i.e., the straggler problem due to devices with limited capabilities and low resource utilization rate of the FL server. The straggler problem arises when devices with limited computational capabilities delay the convergence of the global model. On the other hand, the computational resources of the FL server are often underutilized, mainly due to its relatively simple involvement for model aggregation. To tackle the issues in diverse scenarios, we propose a new joint computational resource allocation and layer partitioning (JCRALP) scheme to improve the overall FL performance by leveraging the capabilities and resources of both FL server and all clients. In the scenario where system parameters regarding the computational capabilities of the clients and the task burden can be accurately measured, we propose an optimization-based approach that leverages our proposed multi-step water-level equalization algorithm and the incremental ceiling adjustment algorithm. In the scenario where parameters cannot be measured accurately, we propose a reinforcement learning-based method using a modified twin delayed deep deterministic policy gradient algorithm. Extensive simulation results demonstrate that JCRALP efficiently and effectively mitigates the straggler problem and inclusively enables more client participation in FL. By including more datasets, the global model becomes more representative, while server computational resources are utilized more efficiently, significantly reducing convergence latency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".