arXiv Machine Learning
Jun 16
DP-Hype: Federated Differentially Private Hyperparameter Search
arXiv:2510. 04902v3 Announce Type: replace Abstract: Tuning hyperparameters in federated machine learning can substantially impact model performance.
Efficient Decentralized Multi-task Dataset Valuation via Model Merging
arXiv:2607. 03346v1 Announce Type: cross Abstract: Accurate and efficient dataset valuation is essential for enabling fair and transparent data marketplaces, especially when multiple contributors provide data for training multi-task models.
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arXiv AI
Jul 1
FedXDS: Leveraging Model Attribution Methods to counteract Data Heterogeneity in Federated Learning
arXiv:2606. 31742v1 Announce Type: cross Abstract: Explainable AI (XAI) methods have demonstrated significant success in recent years at identifying relevant features in input data that drive deep learning model decisions, enhancing interpretability for users.
arXiv Machine Learning
Jun 18
FinP: Fairness-in-Privacy in Federated Learning by Addressing Disparities in Privacy Risk
arXiv:2502. 17748v4 Announce Type: replace Abstract: Federated Learning (FL) inherently mitigates mass data centralization risks; however, its privacy protections are not equally distributed - leaving vulnerable individuals disproportionately exposed to sophisticated privacy attacks.
arXiv AI
Jun 4
Need to Know: Contextual-Integrity-Grounded Query Rewriting for Privacy-Conscious LLM Delegation
arXiv:2606. 04067v1 Announce Type: cross Abstract: As LLMs become increasingly woven into everyday workflows, user queries sent to cloud hosted LLMs routinely mix task-essential content with task non-essential sensitive disclosures, yet type based PII redaction is context agnostic and may raise two issues: over disclosing untyped sensitive context and over removing answer bearing spans.
arXiv AI
Jul 7
Decentralized Aggregation of LLM Predictions via Wagering Mechanisms
arXiv:2607. 04389v1 Announce Type: new Abstract: It is increasingly common to aggregate predictions from multiple LLMs, each with domain expertise or access to private tools and data, to improve collective prediction performance.
arXiv Machine Learning
Jun 2
Differentially Private Datastore Generation for Retrieval-Augmented Inference
arXiv:2606. 01413v1 Announce Type: cross Abstract: It is crucial for modern on-device AI systems that rely on retrieval-augmented inference to release and share datastores without compromising individual privacy.
arXiv AI
1d ago
Privacy-Preserving Decentralized Federated Learning via Explainable Adaptive Differential Privacy
arXiv:2509. 10691v3 Announce Type: replace-cross Abstract: Decentralized federated learning enables collaborative model training without a central server, but shared model updates can still leak sensitive information through inversion, reconstruction, and membership inference attacks.
arXiv AI
Jul 9
Collaborative Synthetic Data Generation for Knowledge Transfer in Federated Learning
arXiv:2607. 07565v1 Announce Type: cross Abstract: One-shot federated learning (OSFL) addresses the communication overhead of federated learning by limiting training to a single round, but doing so without sacrificing model quality is non-trivial, particularly when client data distributions diverge.
arXiv Machine Learning
Jun 30
Efficient Unlearning with Privacy Guarantees
arXiv:2507. 04771v2 Announce Type: replace-cross Abstract: Privacy protection laws, such as the GDPR, grant individuals the right to request the forgetting of their personal data not only from databases but also from machine learning (ML) models trained on them.
arXiv AI
Jun 16
QoS-Aware Token Scheduling and Private Data Valuation for Multi-Modal Agentic Networks
arXiv:2606. 15573v1 Announce Type: new Abstract: In agentic systems, human-generated data records anchor the value of AI services.
arXiv Machine Learning
Jul 7
Shapley-based Data Valuation for LLM Alignment via Sequential Preference Optimization
arXiv:2512. 15765v3 Announce Type: replace Abstract: Data valuation is a natural framework for understanding which preference datasets matter most when aligning a Large Language Model (LLM) using multiple sources.
arXiv AI
Jun 30
Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks
arXiv:2504. 17421v2 Announce Type: replace-cross Abstract: Large language models (LMs) offer broad generalization capabilities but require vast amounts of data and computational resources for domain-specific tasks; small models (SMs), in contrast, are more efficient and tailored to specific domains yet lack general-purpose coverage.