Generalization vs Personalization: A Trade-off for better Data Heterogeneity impact Mitigation in FL
Bibliographic record
Abstract
Federated learning (FL) was introduced recently as a new machine learning (ML) paradigm. It is a distributed network of client nodes that train ML and deep learning (DL) models on their local data without sharing them to preserve data privacy (DP). However, these data are heterogeneous by nature as they are collected in different contexts using various sources such as IoT devices. Consequently, data heterogeneity (DH) in FL has brought new performance-related challenges. Few of these challenges have been addressed in the literature; moreover, context heterogeneity and balance rate were not explored at all. In this paper, we introduce an FL approach in which a trade-off between personalization and generalization is achieved to mitigate the impact of DH and obtain better performance. We focus on three DH challenges: context, non-independent and identically distributed (non-IID) data, and balance rate. For the implementation, fall detection (FD) data is used to demonstrate the potential of our approach in improving the FL system’s performance. FD is an important subject and is particularly prevalent for the safety of elderly people. Hence, we collected fall data from two sensors: accelerometer (ACC) and heart rate (HR), then, we used two ML models to evaluate our approach. We utilized XGBoost (XGB) for balanced and unbalanced clients and One-Class Support Vector Machine (OC-SVM) for one-label clients. Our approach achieved an average F1-score of 88%. A comparative study was also conducted with previous works on FD. Our results showed a performance improvement which exceeded 94.30% on average.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".