Reviewing Federated Learning Aggregation Algorithms; Strategies, Contributions, Limitations and Future Perspectives
Bibliographic record
Abstract
The success of machine learning (ML) techniques in the formerly difficult areas of data analysis and pattern extraction has led to their widespread incorporation into various aspects of human life. This success is due in part to the increasing computational power of computers and in part to the improved ability of ML algorithms to process large amounts of data in various forms. Despite these improvements, certain issues, such as privacy, continue to hinder the development of this field. In this context, a privacy-preserving, distributed, and collaborative machine learning technique called federated learning (FL) has emerged. The core idea of this technique is that, unlike traditional machine learning, user data is not collected on a central server. Nevertheless, models are sent to clients to be trained locally, and then only the models themselves, without associated data, are sent back to the server to combine the different locally trained models into a single global model. In this respect, the aggregation algorithms play a crucial role in the federated learning process, as they are responsible for integrating the knowledge of the participating clients, by integrating the locally trained models to train a global one. To this end, this paper explores and investigates several federated learning aggregation strategies and algorithms. At the beginning, a brief summary of federated learning is given so that the context of an aggregation algorithm within a FL system can be understood. This is followed by an explanation of aggregation strategies and a discussion of current aggregation algorithms implementations, highlighting the unique value that each brings to the knowledge. Finally, limitations and possible future directions are described to help future researchers determine the best place to begin their own investigations.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".