arXiv:2608. 06469v1 Announce Type: cross Abstract: Collaborative machine learning among financial institutions must be both group-fair and robust against deliberate adversarial manipulation.
arXiv:2606. 01719v1 Announce Type: cross Abstract: Machine learning models trained on sensitive data can inadvertently leak population-level information about their training distributions -- a threat known as distribution inference attack (DIA).
arXiv:2605. 01961v2 Announce Type: replace Abstract: Learning from human preference data is becoming a useful tool, from fine-tuning large language models to training reinforcement learning agents.
arXiv:2606. 19129v1 Announce Type: cross Abstract: Dealing simultaneously with confidentiality and Byzantine behaviors in decentralized learning is a challenging problem.
arXiv:2605. 27689v2 Announce Type: replace Abstract: When machine learning systems under-perform for particular subgroups, affected users typically have no way to correct these disparities without relying on platform-level fixes.
FairMean is a new approach for distributed learning that addresses the conflict between fairness and robustness to label poisoning attacks. It assigns weights to client gradients based on a bounded, nondecreasing function of local loss, giving higher weight to high‑loss clients to promote fairness while limiting the influence of poisoned clients. The method is shown to improve fairness compared to standard average‑loss minimization and to reduce accuracy variance while boosting worst‑client accuracy in experiments.
arXiv:2603. 04689v3 Announce Type: replace-cross Abstract: Fair top-$k$ selection, which ensures appropriate proportional representation of members from minority or historically disadvantaged groups among the top-$k$ selected candidates, has drawn significant attention.
arXiv:2602.04125v2 Announce Type: replace-cross Abstract: Modern digital platforms use contextual bandits to allocate valuable exposure and opportunities among competing participants. Fair treatment...
The paper introduces Fulcrum, a topology‑aware differential privacy scheme for hierarchical federated learning that allocates noise based on the size and exposure of regional aggregation groups. By deriving a closed‑form exposure dispersion metric from region structure and weights, the method optimally balances privacy and utility, achieving up to 14.84% accuracy gains on image tasks and 12.16% on text tasks at ε = 0.99 compared to uniform noise allocation. The approach ensures each participant receives noise commensurate with its actual exposure, eliminating unnecessary privacy overhead.
Dealing simultaneously with confidentiality and Byzantine behaviors in decentralized learning is a challenging problem. Indeed, in decentralized learning, clients train a machine learning model while keeping their data locally and share their model parameters or gradients with a set of neighbors.
arXiv:2609.21057v1 Announce Type: new Abstract: Federated learning (FL) enables collaborative model training without sharing raw data, but its performance degrades under non-IID data and stochastic c...
The paper introduces DP‑BR‑FedAvg, a federated learning framework that combines Gaussian‑mechanism differential privacy with a coordinate‑wise trimmed‑mean Byzantine‑robust aggregation rule. It is evaluated on a simulated cross‑institutional classification task involving fraud and clinical‑risk scoring, where it improves the F1‑score for a minority class from 0.030 (plain FedAvg) to 0.119 while bounding privacy loss. The study demonstrates that privacy and robustness mechanisms interact, and that system design for regulated, adversarial, cross‑institutional settings must account for this interaction.
arXiv:2605. 07663v2 Announce Type: replace-cross Abstract: Data valuation methods allocate payments and audit training data's contribution to machine-learning pipelines; however, they often assume passive contributors.