DeFedGCN: Privacy-Preserving Decentralized Federated GCN for Recommender System
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Federated recommender system (RS), a prevailing distributed paradigm, has been spawning significant interest in exploiting locally stored but tremendous data to predict items best aligned with clients. However, federated RS suffers severely from a single point of failure due to the dependency on the central server, leading to potential denial of service (DoS) attacks. To address this security weakness, in this paper, we propose a decentralized privacy-preserving federated graph convolutional network for RS, dubbed DeFedGCN. Specifically, DeFedGCN aggregates local updates by a decentralized consensus-reaching process and customizes local models for personalized recommendation, where the aggregation is enhanced by local differential privacy to resist model inversion attacks. More importantly, to promote the recommendation performance, DeFedGCN conducts a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sub-graph expansion</i> based on the private set interaction to explore high-order interactions among clients and items. Theoretical analysis confirms the effectiveness and privacy guarantee of DeFedGCN. Additionally, we conduct extensive experiments on four widespread real-world databases. The recommendation performance of DeFedGCN outperforms the state-of-the-art federated RS algorithms without security protection against DoS attacks by up to 7.4%.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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