FEDERATED LEARNING-AWARE MULTI-OBJECTIVE SCHEDULING FOR DISTRIBUTED EDGE-CLOUD ENVIRONMENTS
Bibliographic record
Abstract
The proliferation of Internet of Things applications and latency-sensitive computing tasks has necessitated novel approaches to resource management across distributed edge-cloud architectures. Federated Learning has emerged as a compelling paradigm for collaborative model training without centralizing data, yet integrating Federated Learning into multi-objective scheduling frameworks presents significant challenges. This paper proposes a comprehensive scheduling strategy that accounts for Federated Learning-specific requirements including model convergence time, communication overhead, and data heterogeneity while optimizing multiple conflicting objectives such as makespan, energy consumption, and resource utilization. We develop a hierarchical scheduling architecture that coordinates tasks between mobile edge computing servers, base stations, and cloud data centers while maintaining Federated Learning protocol integrity. The proposed approach employs an adaptive multi-objective optimization algorithm with dynamic crossover and mutation probabilities that adjusts resource allocation based on Federated Learning training progress and system state. Experimental evaluation demonstrates that our Federated Learning-aware scheduling strategy with adaptive genetic algorithm parameters achieves superior performance compared to fixed-parameter approaches, reducing total system cost by approximately 28 percent while improving convergence speed by 35 percent. The framework effectively balances computation offloading decisions with Federated Learning communication patterns across the hierarchical mobile edge computing infrastructure, resulting in enhanced system efficiency for distributed edge-cloud environments. This work establishes foundations for integrating Federated Learning workflows into production-scale distributed computing infrastructures while addressing the unique challenges posed by privacy-preserving machine learning paradigms.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.002 |
| 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 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".