On the Impact of Malicious and Cooperative Clients on Validation Score-Based Model Aggregation for Federated Learning
Bibliographic record
Abstract
Conventional AI-based service flow remains a challenge for IoT-enabled devices since data collected by local clients is transferred to a centralized server, which contains a global machine learning (ML) model. However, this introduces privacy and security concerns for the clients, and federated Learning is positioned to overcome this problem where each client trains a local model with its local data and shares its model parameters with the centralized server instead of sharing data. Upon the receipt of all parameters, it aggregates these parameters and generates a new global model. Later this global model is distributed among the clients. Various aggregation methods have been published for increasing the global model's accuracy performance after aggregation. However, those new aggregation algorithms are not fully investigated under malicious and collaborated environments. A malicious environment is a scenario where malicious clients are present and can share parameters to degrade the aggregated model performance. On the other hand, the collaborative environment is another scenario in which some clients can share information with each other in order to collaborate. To tackle this issue, we investigate a new aggregation method called Score Based Aggregation (SBA) That aims to mitigate the impact of the model parameters from such malicious clients without keeping compromising the training accuracy. We compare our result to a baseline approach where the malicious client is varied from 20% to 50%. Numerical results suggest that the SBA aggregation helps the model maintain the convergence of accuracy at higher levels in comparison to the baseline approach.
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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 | 0.009 | 0.029 |
| 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 | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".