Search: “privacy”

Maverick: Private and Verifiable LLM Inference Made Practical via Matrix-Vector Multiplication Delegation

arXiv:2609.10264v1 Announce Type: cross Abstract: Open-source large language models (LLMs) are increasingly competitive with closed-source models while offering transparency and the ability to run inf

10 Sept 2026

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PELM: Power Efficient On-Device LLM Inference with Speculative Decoding and Dynamic Voltage Frequency Scaling

DGX agent

arXiv:2609.09662v1 Announce Type: cross Abstract: Deploying Large Language Models (LLMs) directly on mobile platforms at the edge is gaining traction due to a myriad of benefits, such as increased pri

local-aiarxiv-cs-cl

10 Sept 2026

BoundarySeg:Boundary-Aware Multi-Task Learning for Semi-Supervised 3D Medical Image Segmentation with Limited Unlabeled Data

arXiv:2505.09829v2 Announce Type: replace Abstract: Data scarcity is a primary bottleneck in 3D medical image segmentation: expert voxel-level annotations are expensive, and privacy regulations often

FANS: Federated Adaptive Network Search Learning for Heterogeneous Devices

arXiv:2609.06106v1 Announce Type: new Abstract: Heterogeneous Federated Learning (HFL) aims to train models across devices with diverse resource budgets while preserving data privacy. Existing HFL met

Federated Binary Gating with Server-Side Vision-Language Inference for Surveillance Anomaly Classification

arXiv:2609.07403v1 Announce Type: new Abstract: Privacy-sensitive surveillance systems could benefit from large vision-language models (VLMs), but such models typically require centralized access to r

Fine-grained Distributed Backdoor Attacks in Federated Learning

arXiv:2609.07147v1 Announce Type: new Abstract: Federated learning, as a privacy-preserving distributed machine learning paradigm, faces significant threats from backdoor attacks. Compared to centrali

researcharxiv-cs-lg

TASTE: Throughput-Aware Batch Size Tuning for On-Device Edge Learning

arXiv:2609.07444v1 Announce Type: new Abstract: The rise of privacy-preserving artificial intelligence (AI) has shifted the focus of model adaptation and personalization towards on-device learning, wh

OpenAI alleged of stealing mathematicians work

Privacy have been concern of many of us to have their own hardware to run llms, and here's another reason why: two mathematicians spent a year cracking one of the hardest problems in math and fed ever

local-air-localllama

8 Sept 2026

LeanStream: A Speculate-and-Refine Streaming Framework for Efficient on-Device LLM Inference

arXiv:2609.03079v1 Announce Type: new Abstract: On-device LLM inference is attractive for privacy and responsiveness, but remains challenging on mobile and embedded devices because model weights far e

4 Sept 2026

Mind the Gap: Robustness Risks in PII Detection Systems

arXiv:2609.03464v1 Announce Type: new Abstract: Personally Identifiable Information (PII) detection is a foundational component of data protection infrastructure where missed entities constitute direc

4 Sept 2026

PragAlign: Feedback-Guided Pragmatic Alignment for Controlled Synthetic Dialogue Generation

arXiv:2609.02480v1 Announce Type: new Abstract: Synthetic dialogue generation can support research in privacy-restricted service settings, but generated conversations must preserve communicative inten

safetyarxiv-cs-cl

3 Sept 2026

Similarity-Aware Personalized Federated Learning in Heterogeneous Environments

arXiv:2609.02241v1 Announce Type: new Abstract: Federated Learning (FL) allows decentralized clients to train models collaboratively while preserving data privacy. However, distribution mismatch acros

3 Sept 2026

Breaking the Structural Identity: Personalized Federated LoRA Fine-tuning under Rank Heterogeneity

arXiv:2609.00632v1 Announce Type: cross Abstract: Large Language Models (LLMs) have achieved remarkable success across diverse domains, but their adaptation to privacy-sensitive, distributed datasets

researcharxiv-cs-ai

2 Sept 2026

EGT-KG: Evidence-Grounded Typed KG Retrieval for Practical Scientific QA with Small Language Models

model-releasesarxiv-cs-ai

arXiv:2609.00479v1 Announce Type: new Abstract: For emerging scientific research domains, local Small Language Models (SLMs) are becoming more attractive, as they offer stronger privacy control and mo

2 Sept 2026

Gaussian Core LoRA: Distribution-Aware Dynamic Adaptation for Broad Concept Erasure

arXiv:2609.01433v1 Announce Type: new Abstract: Concept erasure aims to suppress unsafe, privacy-sensitive, or undesirable generations in text-to-image diffusion models while preserving benign semanti

2 Sept 2026

An evidence-guided reinforcement learning method to improve psychiatric reasoning in small language models

arXiv:2602.06449v2 Announce Type: replace Abstract: Privacy and computational constraints limit the use of large language models in psychiatry, while adapting small language models (SLMs) often requir

safetyarxiv-cs-cl

Federated Learning for MRI-based BrainAGE: a multicenter study on post-stroke functional outcome prediction

arXiv:2506.15626v3 Announce Type: replace-cross Abstract: extbf{Objective:} Brain-predicted age difference (BrainAGE) is a neuroimaging biomarker reflecting brain health. However, training robust Brai

researcharxiv-cs-ai

Applications

H-FedSN: Personalized Sparse Networks for Efficient and Accurate Hierarchical Federated Learning for IoT Applications

arXiv:2412.06210v3 Announce Type: replace Abstract: With the rapid development of the Internet of Things (IoT), federated learning (FL) has gained increasing attention for its privacy-preserving use o

applicationsarxiv-cs-lg

Agents

MAGneT: Coordinated Multi-Agent Generation of Synthetic Multi-Turn Mental Health Counseling Sessions

arXiv:2509.04183v3 Announce Type: replace-cross Abstract: The growing demand for scalable psychological counseling highlights the need for high-quality, privacy-compliant data, yet such data remains s

agentsarxiv-cs-ai

Multi-Scale Temporal Domain Alignment for Federated Video Domain Adaptation

arXiv:2608.29186v1 Announce Type: new Abstract: Federated Video Domain Adaptation (FVDA) enables collaborative learning across distributed and non-IID video datasets while preserving privacy, but is u

On the Plasticity Collapse in Continual Machine Unlearning

model-releasesarxiv-cs-ai

arXiv:2608.29513v1 Announce Type: cross Abstract: Machine unlearning enables deep neural networks to selectively remove the influence of specific data in response to privacy and regulatory requirement

Applications

Spatial Entropy based Partitioning for Spatiotemporal Graph Unlearning

arXiv:2608.29360v1 Announce Type: new Abstract: Spatiotemporal graphs underpin applications such as traffic forecasting, weather forecasting, and healthcare monitoring. Privacy regulations such as the

applicationsarxiv-cs-lg

How Far Can 5,500 Hours of Driving Take You? A Scaling Law Analysis of Video Diffusion Models

arXiv:2608.28404v1 Announce Type: new Abstract: Video generation for autonomous driving cannot follow the web-scale route: driving data is expensive to collect, bound by privacy requirements, and cann

31 Aug 2026

Initialization Is Critical: Advancing Federated Short-Term Load Forecasting under Load Heterogeneity via Model Initialization

arXiv:2608.27791v1 Announce Type: new Abstract: Short-term load forecasting (STLF) provides essential information for numerous applications in modern power systems. However, accurate STLF often relies

31 Aug 2026

Roleplaying with Structure: Synthetic Therapist-Client Conversation Generation from Questionnaires

arXiv:2510.25384v2 Announce Type: replace Abstract: Large Language Models (LLMs) are promising tools for synthetic data generation in mental health. However, privacy policies and restrictions forced p

researcharxiv-cs-cl

31 Aug 2026

Federated Adversarial Training with Transformers

arXiv:2206.02131v2 Announce Type: replace-cross Abstract: Federated learning (FL) has emerged to enable global model training over distributed clients' data while preserving its privacy. However, the

28 Aug 2026

Cooperative Multi-Agent Reinforcement Learning for Adaptive Aggregation in Semi-Supervised Federated Learning with non-IID Data

arXiv:2608.25794v1 Announce Type: new Abstract: Federated Learning (FL) enables distributed training of machine learning models while preserving data privacy. However, FL struggles with heterogeneous,

safetyarxiv-cs-lg

27 Aug 2026

Applications

GNNBleed: Inference Attacks to Unveil Private Edges in Graphs with Realistic Access to GNN Models

arXiv:2311.16139v3 Announce Type: replace-cross Abstract: Graph Neural Networks (GNNs) have become indispensable tools for learning from graph structured data, catering to various applications such as

applicationsarxiv-cs-lg

26 Aug 2026

Native-Space 3D CarveMix for Multi-Site T1w Stroke Segmentation

arXiv:2608.23882v1 Announce Type: cross Abstract: Segmenting ischemic stroke lesions on T1-weighted (T1w) MRI acquired across different scanners and protocols without intensity standardization is diff

26 Aug 2026

Agents

An Information-Flow Perspective on Explainability Requirements: Specification and Verification

arXiv:2509.01479v3 Announce Type: replace-cross Abstract: Explainable systems expose information about why certain observed effects are happening to the agents interacting with them. We argue that thi

agentsarxiv-cs-ai

CiUNet: A Hybrid Swin-CNN UNet for Medical Image Segmentation

arXiv:2608.22281v1 Announce Type: cross Abstract: Medical image segmentation requires high accuracy and robustness, yet practical commercial deployment also demands privacy preservation and computatio

local-aiarxiv-cs-cv

Close Shortcut Wins Long: Seeking Diverse and Stable Generators for Data-Free Knowledge Distillation

arXiv:2608.22003v1 Announce Type: new Abstract: Data-Free Knowledge Distillation (DFKD) preserves privacy by transferring knowledge without real data access. However, existing generator-based DFKD met

FedRP: A Communication-Efficient Approach for Differentially Private Federated Learning Using Random Projection

arXiv:2509.10041v2 Announce Type: replace Abstract: Federated learning (FL) enables collaborative model training without centralizing data, but exchanging high-dimensional updates can expose sensitive

Tutorials

GeoMix: Descriptor-Free Visual Localization via Global Context and Multi-Detector Training

arXiv:2607.02486v2 Announce Type: replace Abstract: Descriptor-free visual localization eliminates high-dimensional descriptor storage, preserves scene privacy, and simplifies map maintenance, yet its

tutorialsarxiv-cs-cv

More Accurate or More Efficient? Evaluating Locally Deployed Compact Open-Weight Language Models for Mathematical Reasoning

arXiv:2608.22048v1 Announce Type: new Abstract: Large language models are increasingly deployed on local hardware for privacy, cost, and accessibility reasons. Yet many evaluations emphasize accuracy

local-aiarxiv-cs-ai

Unlearning Is Not Just Erasing: Temporal Decoupling via Generation Inequality

arXiv:2608.23020v1 Announce Type: cross Abstract: Large language models (LLMs) require effective unlearning to address privacy regulations and safety concerns. However, achieving precise forgetting wi

local-aiarxiv-cs-ai

Beyond Effectiveness: A Multi-Criteria Framework for Comparing Practical Socio-Technical Interventions

arXiv:2608.20649v1 Announce Type: new Abstract: Designers and policymakers in sociotechnical domains like content moderation, privacy interfaces, recommender systems and beyond, must choose among a gr

researcharxiv-cs-ai

24 Aug 2026

BIPPO: Budget-Aware Independent PPO for Energy-Efficient Federated Learning Services

arXiv:2511.08142v2 Announce Type: replace Abstract: Federated Learning (FL) is a promising machine learning solution in large-scale IoT systems, guaranteeing load distribution and privacy. However, FL

safetyarxiv-cs-lg

24 Aug 2026

Federated and differentially private estimation of KL divergence

arXiv:2411.16478v3 Announce Type: replace Abstract: Measuring distribution drifts is a key task in managing distributed, sensitive data, as it underpins a wide range of federated learning and analytic

24 Aug 2026

Distilling Aggregate Mobility Statistics into a Language Model Policy for Post-Event Crowd Simulation

arXiv:2608.19778v1 Announce Type: cross Abstract: Pedestrian simulators need a behaviour rule for every agent, but privacy usually limits the data for setting one to aggregate statistics, namely zone-

safetyarxiv-cs-ai

21 Aug 2026

MOSAIC: Modality-agnostic Spectral Alignment for Federated Image-level Weakly Supervised Tumor Segmentation under Client-specific Missing Modalities

arXiv:2608.19788v1 Announce Type: cross Abstract: Trustworthy multimodal fusion in clinical settings requires handling incomplete and heterogeneous modality subsets across institutions, where privacy

safetyarxiv-cs-cv

21 Aug 2026

STEP: Score-Based Temporal Energy for Human Pose Video Anomaly Detection

arXiv:2608.19987v1 Announce Type: new Abstract: Skeleton-based Video Anomaly Detection (VAD) offers a robust, privacy-preserving solution for identifying abnormal behaviors. To model the distribution

21 Aug 2026

Expanding Google Antigravity for enterprise customers

model-releasesgoogle-cloud-ai

Since announcing Google Antigravity in Gemini Enterprise Agent Platform at I/O in May, we’ve heard helpful feedback from our customers. Your developers want easy access to coding agents across surface

Gradient Mirage: Trainable yet Label-Unidentifiable Gradients in Large Language Model Split Learning

arXiv:2608.18767v1 Announce Type: new Abstract: Gradient matching attacks (GMAs) in LLM split learning (SL) rely on a critical yet underexplored assumption: the gradient exposed at the split interface

researcharxiv-cs-cl

Is your coding agent uploading all your code?

After Grok Build was caught uploading entire Git repos, we read the privacy docs for Claude Code, Codex, Cursor, GitHub Copilot, and Grok Build to compare what code leaves your machine, what gets reta

model-releasesarize-ai

Redakto - The Incognito Tab for LLMs

arXiv:2608.18260v1 Announce Type: new Abstract: Large Language Models (LLMs) are being increasingly used in everyday applications. A major challenge in the context of LLMs or Artificial Intelligence (

local-aiarxiv-cs-ai

SCORE: Subject Coordinate Recovery for Label-Free Cross-Subject EEG-to-Image Retrieval

arXiv:2608.19134v1 Announce Type: new Abstract: Accurate visual decoding can reveal how the brain represents visual information and recover perceived content from neural signals such as electroencepha

safetyarxiv-cs-lg

We've been working on this with customers for a while. Mythos-class models require additional safety measures and enterprises need to meet t…

We've been working on this with customers for a while. Mythos-class models require additional safety measures and enterprises need to meet their own privacy and compliance rules. Customers can own and

safetyboris-cherny--x