Search: “privacy”

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

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Applications

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

DGX agent

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

researcharxiv-cs-cv

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

researcharxiv-cs-cv

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

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

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

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-

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

model-releasesarxiv-cs-cv

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

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

20 Aug 2026

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 (

20 Aug 2026

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

20 Aug 2026

Benchmarking the Benchmarks: Evaluating Automated Safety Benchmarks for Small Language Models

arXiv:2608.17183v1 Announce Type: new Abstract: Small Language Models (SLMs) are increasingly deployed in resource-constrained, privacy-sensitive settings, where safety and bias failures can cause sec

19 Aug 2026

Cluster Aggregated GAN (CAG): A Cluster-Based Hybrid Model for Appliance Pattern Generation

arXiv:2512.22287v4 Announce Type: replace-cross Abstract: Synthetic appliance data are essential for developing non-intrusive load monitoring algorithms and enabling privacy preserving energy research

19 Aug 2026

The Acknowledgment Point Is the System: Durable Policy-Decision Receipts for AI Audit Evidence

arXiv:2608.17176v1 Announce Type: cross Abstract: An AI audit record is useful only if its durability and trust boundary are explicit. Returning a guarded decision before any durable write minimizes l

19 Aug 2026

Adaptive Heterogeneous Compression for Resource-Efficient Federated Knowledge Distillation

arXiv:2608.15660v1 Announce Type: cross Abstract: Federated learning (FL) enables privacy-preserving distributed model training but faces challenges from heterogeneous model architectures and limited

FedImp: Enhancing Federated Learning Convergence with Impurity-Based Weighting

arXiv:2608.14654v1 Announce Type: cross Abstract: Federated Learning (FL) is a collaborative paradigm that enables multiple devices to train a global model while preserving local data privacy. A major

Global Federated Learning Strategies for Building Efficient Personalized Models

arXiv:2608.15107v1 Announce Type: new Abstract: Federated learning (FL) is a practical framework that can train models on distributed user data while guaranteeing data privacy; however, due to heterog

local-aiarxiv-cs-lg

KOALA: Koopman Operator Learning for WiFi-Based Anticipatory Hum

arXiv:2608.15815v1 Announce Type: new Abstract: WiFi Channel State Information (CSI) has emerged as a privacy-preserving alternative to cameras for human pose estimation. However, existing approaches

researcharxiv-cs-lg

Learning to Unlearn: Machine Unlearning via Learning the Unlearning Behaviors

arXiv:2608.16700v1 Announce Type: cross Abstract: Various machine unlearning techniques have been developed in response to privacy legislation requirements, enabling individuals to exercise their lega

MoE Router-Guided Clustering for Heterogeneous Federated Instruction Tuning

arXiv:2608.15311v1 Announce Type: new Abstract: Federated instruction fine-tuning enables Large Language Models (LLMs) to adapt to decentralized, privacy-sensitive data without requiring data sharing.

AlignFace: Human-Aligned Face Similarity Metric with Interpretable Concept Relations

arXiv:2608.14130v1 Announce Type: cross Abstract: Computer vision models for generated facial content, such as face editing and privacy protection, increasingly affect people, requiring similarity met

17 Aug 2026

Simulation-Driven Vehicular Traffic Data Augmentation: Extending Sensor Coverage Through Virtual Sensing

arXiv:2608.13993v1 Announce Type: new Abstract: Urban traffic management relies on sensor networks whose spatial coverage is limited by deployment costs and privacy regulations. Machine learning model

17 Aug 2026

A Prior-Aware Metric for Efficiently Distinguishing Memorization from Generalization in Large Language Models

arXiv:2602.18733v2 Announce Type: replace Abstract: Training data leakage from Large Language Models (LLMs) raises serious concerns related to privacy, security, and copyright compliance. A central ch

14 Aug 2026

CoMedBench: A Multi-Source Benchmark of Synthetic Medical Data Fidelity and Downstream Utility

arXiv:2608.12805v1 Announce Type: new Abstract: Access to clinical data is essential for developing reliable healthcare machine learning systems, but direct use of electronic health records is constra

14 Aug 2026

Understanding Backdoor Vulnerabilities in Vertical Federated Learning: The Gap Between Research and Practice

arXiv:2608.12962v1 Announce Type: new Abstract: Vertical Federated Learning (VFL) enables organizations holding complementary features of shared entities to collaborate and train models. In this setti

14 Aug 2026

Applications

Hierarchical Federated Transfer Learning in Digital Twin-Based Vehicular Networks

arXiv:2608.11532v1 Announce Type: cross Abstract: In recent research on the Digital Twin-based Vehicular Ad hoc Network(DT-VANET), Federated Learning (FL) has shown its ability to provide data privacy

applicationsarxiv-cs-ai

13 Aug 2026

LEMUR: Latent Entropy-aware Multimodal Unlearning via Visual-anchored Reasoning Redirection

arXiv:2608.11691v1 Announce Type: cross Abstract: Reinforcement-learning (RL) post-training equips multimodal large reasoning models (MLRMs) with exploratory chains of thought (CoT), substantially imp

researcharxiv-cs-cl

13 Aug 2026

Calibrating Post-Training Feature Shifts for LLM Data Contamination Detection

arXiv:2608.10462v1 Announce Type: new Abstract: Large language models (LLMs) are trained on massive and largely undisclosed corpora that may contain copyrighted or privacy-sensitive content. Data cont

researcharxiv-cs-cl

12 Aug 2026

Compositional Benchmark Synthesis for Hierarchical Human Action Recognition

arXiv:2608.10765v1 Announce Type: new Abstract: Recognizing human behavior across levels of abstraction, from atomic actions to long-horizon intentions, requires data annotated along a semantic hierar

12 Aug 2026

Locally Deployable Small Language Models for Emergency Department Decision Support: A Systematic Benchmark of Fine-Tuning Strategies

arXiv:2608.10273v1 Announce Type: cross Abstract: Deploying large language models (LLMs) for decision support in emergency departments (EDs) faces two major challenges: privacy risks of transmitting p

12 Aug 2026

Communication-efficient distributed hazard difference estimation for heterogeneous multi-site survival data

arXiv:2601.14609v2 Announce Type: replace-cross Abstract: Multi-site collaboration can power survival models that no single hospital could fit alone, but privacy rules and protected computing environm

Applications

Deep probabilistic logic programming for diagnostic reasoning from incomplete information: A case study in stroke detection

arXiv:2608.08561v1 Announce Type: new Abstract: In medical applications, raw data is frequently associated with significant privacy concerns, lending particular importance to the encoding of summary s

applicationsarxiv-cs-ai

EFFEKT: Efficient Federated Knowledge Transfer to Foundation Models

arXiv:2608.08138v1 Announce Type: new Abstract: Recent data protection laws have accelerated the adoption of Federated Learning (FL) for privacy-preserving decentralized training. Nevertheless, increa

safetyarxiv-cs-cv

FedTVD: Balancing Data Quality and Quantity for Robust Federated Learning

arXiv:2608.09221v1 Announce Type: cross Abstract: Federated Learning (FL) enables collaborative model training across distributed client devices while preserving data privacy. However, FL faces signif

Label Granularity Skew in Federated Learning with Hierarchical Image Classification

arXiv:2608.09236v1 Announce Type: new Abstract: Federated learning enables privacy-preserving collaboration across distributed devices without centralizing local data. However, clients may differ not

local-aiarxiv-cs-lg

Multimodal Federated Learning under Dual-Axis Modality Missingness

arXiv:2608.09240v1 Announce Type: cross Abstract: Multimodal federated learning (FL) supports collaborative modeling in privacy-sensitive health-sensing and medical settings, but realistic deployments

PressureMesh: 3D Human Mesh Estimation from Multi-Device Pressure Images

arXiv:2608.09550v1 Announce Type: new Abstract: Human pose monitoring is crucial in fields such as rehabilitation assessment and human-computer interaction. Due to its privacy-preserving nature, press

researcharxiv-cs-cv

ZeroLock: Concurrent Memory-Efficient LLM Training via Modular Update Decoupling

arXiv:2608.07974v1 Announce Type: new Abstract: Large language model (LLM) fine-tuning at the edge adapts the model to scenario-specific data while preserving privacy. Although existing studies propos

local-aiarxiv-cs-lg

FedLBW: A Loss-Based Weighting Strategy for Federated Learning on Non-IID Data in Wireless Networks

arXiv:2608.07007v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative machine learning (ML) across distributed clients while preserving privacy. However, efficient model conver

10 Aug 2026

GRASP: Reinforcing Language Model Anonymizers with Group Relative Policy Optimization