Secure and Efficient Federated Learning Against Model Poisoning Attacks in Horizontal and Vertical Data Partitioning
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Bibliographic record
Abstract
In distributed systems, data may partially overlap in sample and feature spaces, that is, horizontal and vertical data partitioning. By combining horizontal and vertical federated learning (FL), hybrid FL emerges as a promising solution to simultaneously deal with data overlapping in both sample and feature spaces. Due to its decentralized nature, hybrid FL is vulnerable to model poisoning attacks, where malicious devices corrupt the global model by sending crafted model updates to the server. Existing work usually analyzes the statistical characteristics of all updates to resist model poisoning attacks. However, training local models in hybrid FL requires additional communication and computation steps, increasing the detection cost. In addition, due to data diversity in hybrid FL, solutions based on the assumption that malicious models are distinct from honest models may incorrectly classify honest ones as malicious, resulting in low accuracy. To this end, we propose a secure and efficient hybrid FL against model poisoning attacks. Specifically, we first identify two attacks to define how attackers manipulate local models in a harmful yet covert way. Then, we analyze the execution time and energy consumption in hybrid FL. Based on the analysis, we formulate an optimization problem to minimize training costs while guaranteeing accuracy considering the effect of attacks. To solve the formulated problem, we transform it into a Markov decision process and model it as a multiagent reinforcement learning (MARL) problem. Then, we propose a malicious device detection (MDD) method based on MARL to select honest devices to participate in training and improve efficiency. In addition, we propose an alternative poisoned model detection (PMD) method considering model change consistency. This method aims to prevent poisoned models from being used in the model aggregation. Experimental results validate that under the random local model poisoning attack, the proposed MDD method can save over 50% training costs while guaranteeing accuracy. When facing the advanced adaptive local model poisoning (ALMP) attack, utilizing both the proposed MDD and PMD methods achieves the desired accuracy while reducing execution time and energy consumption.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 it