Decentralized Federated Learning with Adaptive Aggregator Selection
Bibliographic record
Abstract
Federated Learning (FL) has emerged as an indispensable paradigm for privacy-preserving collaborative model training across distributed clients. Yet, conventional FL frameworks depend on a central server for aggregation, creating scalability limits, bias, and single-point vulnerabilities. Our previous work, FL-EGM, introduced a decentralized FL architecture that elects an aggregator in every round based on its validation accuracy. The elected aggregator abstains from local training, aggregates updates from other clients, and then refines the global model on its raw data, producing an Enhanced Global Model (EGM). Empirical evaluation yielded 98.54% accuracy and strong robustness against biased aggregators. This doctoral research extends FL-EGM toward a middleware-oriented decentralized FL framework that incorporates resource-aware client selection in adversarial training, energy-efficient scheduling, and incentive-driven participation. The goal is to build resilient, sustainable, and fair FL infrastructures capable of large-scale heterogeneous deployment.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| 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".