Accelerating Federated Learning for Edge Intelligence Using Conjugated Central Acceleration With Inexact Global Line Search
Bibliographic record
Abstract
Driven by the increasing demand for real-time, low-latency learning processes and the ever-growing emphasis on data privacy, Federated Learning (FL) enabled edge intelligence emerges as a promising decentralized learning paradigm at the edge of the network, which empowers collaborative model training on edge agents, allowing them to make intelligent decisions locally without relying solely on centralized cloud servers. To enhance the training efficiency of edge agents and alleviate communication burdens, we propose a novel technique called Conjugated Central Acceleration with Inexact Line Search enabled Federated Stochastic Variance Reduced Gradient (CLSFSVRG). Conjugate Central Acceleration leverages conjugate gradient technique to efficiently utilize the training information from multiple edge agents by additional updating efforts in the central server, thereby enhancing the convergence rates of the global model and reduce the local training burden. Inexact Line Search optimizes the step size for model updates, striking a balance between precision and computational efficiency. Simulation results demonstrate that the proposed scheme outperforms the state-of-the-art FL algorithms, achieving superior performance in terms of higher test accuracy and faster convergence speed. Remarkably, our approach reduces communication costs by an impressive 82.4%, while still achieving a test accuracy of 96.5%. By allowing a small portion of edge agents to participate, CLSFSVRG exhibits higher robustness without compromising the test accuracy. Moreover, the fast convergence speed achieved with a limited number of participating edge agents contributes to significant reductions in edge computing cost during the training procedure.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".