Daily Arxiv

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VerilogLAVD: LLM-Aided Rule Generation for Vulnerability Detection in Verilog

HRS: Hybrid Representation Framework with Scheduling Awareness for Time Series Forecasting in Crowdsourced Cloud-Edge Platforms

Atom-Searcher: Enhancing Agentic Deep Research via Fine-Grained Atomic Thought Reward

Vehicle detection from GSV imagery: Predicting travel behavior for cycling and motorcycling using Computer Vision

CRED-SQL: Enhancing Real-world Large Scale Database Text-to-SQL Parsing through Cluster Retrieval and Execution Description

Robust Federated Learning under Adversarial Attacks via Loss-Based Client Clustering

SRMA-Mamba: Spatial Reverse Mamba Attention Network for Pathological Liver Segmentation in MRI Volumes

MedKGent: A Large Language Model Agent Framework for Constructing Temporally Evolving Medical Knowledge Graph

Fortifying the Agentic Web: A Unified Zero-Trust Architecture Against Logic-layer Threats

Generalized invariants meet constitutive neural networks: A novel framework for hyperelastic materials

What Matters for Bioacoustic Encoding

Age-Normalized HRV Features for Non-Invasive Glucose Prediction: A Pilot Sleep-Aware Machine Learning Study

Comparative Analysis of Time Series Foundation Models for Demographic Forecasting: Enhancing Predictive Accuracy in US Population Dynamics

FreeGAD: A Training-Free yet Effective Approach for Graph Anomaly Detection

An Explainable AI based approach for Monitoring Animal Health

Exploring Content and Social Connections of Fake News with Explainable Text and Graph Learning

Can AI Keep a Secret? Contextual Integrity Verification: A Provable Security Architecture for LLMs

Fine-Grained Safety Neurons with Training-Free Continual Projection to Reduce LLM Fine Tuning Risks

To Theoretically Understand Transformer-Based In-Context Learning for Optimizing CSMA

MAViS: A Multi-Agent Framework for Long-Sequence Video Storytelling

Towards Theoretical Understanding of Transformer Test-Time Computing: Investigation on In-Context Linear Regression

Early Detection of Pancreatic Cancer Using Multimodal Learning on Electronic Health Records

A Survey of LLM-based Deep Search Agents: Paradigm, Optimization, Evaluation, and Challenges

FlowState: Sampling Rate Invariant Time Series Forecasting

Automatic Image Colorization with Convolutional Neural Networks and Generative Adversarial Networks

Unlocking the Potential of MLLMs in Referring Expression Segmentation via a Light-weight Mask Decoder

SlotMatch: Distilling Temporally Consistent Object-Centric Representations for Unsupervised Video Segmentation

SBP-YOLO:A Lightweight Real-Time Model for Detecting Speed Bumps and Potholes

Dataset Condensation with Color Compensation

A Versatile Pathology Co-pilot via Reasoning Enhanced Multimodal Large Language Model

Spatial-Temporal Transformer with Curriculum Learning for EEG-Based Emotion Recognition

RAPNet: A Receptive-Field Adaptive Convolutional Neural Network for Pansharpening

Penalizing Infeasible Actions and Reward Scaling in Reinforcement Learning with Offline Data

Identify, Isolate, and Purge: Mitigating Hallucinations in LVLMs via Self-Evolving Distillation

Tensor Program Optimization for the RISC-V Vector Extension Using Probabilistic Programs

Segment Anything in Pathology Images with Natural Language

Neural Cellular Automata for ARC-AGI

Scaling Intelligence: Designing Data Centers for Next-Gen Language Models

ConTextTab: A Semantics-Aware Tabular In-Context Learner

PlantDeBERTa: An Open Source Language Model for Plant Science

Recipes for Pre-training LLMs with MXFP8

Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning

Sample Complexity of Diffusion Model Training Without Empirical Risk Minimizer Access

"Haet Bhasha aur Diskrimineshun": Phonetic Perturbations in Code-Mixed Hinglish to Red-Team LLMs

VoiceCloak: A Multi-Dimensional Defense Framework against Unauthorized Diffusion-based Voice Cloning

LaDi-WM: A Latent Diffusion-based World Model for Predictive Manipulation

Position: We Need Responsible, Application-Driven (RAD) AI Research

Blending 3D Geometry and Machine Learning for Multi-View Stereopsis

Harnessing Structured Knowledge: A Concept Map-Based Approach for High-Quality Multiple Choice Question Generation with Effective Distractors

Hallucinations and Key Information Extraction in Medical Texts: A Comprehensive Assessment of Open-Source Large Language Models

POPri: Private Federated Learning using Preference-Optimized Synthetic Data

Parameter-Efficient Continual Fine-Tuning: A Survey

Unleashing the Power of LLMs in Dense Retrieval with Query Likelihood Modeling

Augmented Adversarial Trigger Learning

Setup Once, Secure Always: A Single-Setup Secure Federated Learning Aggregation Protocol with Forward and Backward Secrecy for Dynamic Users

DDD-GenDT: Dynamic Data-driven Generative Digital Twin Framework

Understanding and Mitigating Memorization in Generative Models via Sharpness of Probability Landscapes

Script-Strategy Aligned Generation: Aligning LLMs with Expert-Crafted Dialogue Scripts and Therapeutic Strategies for Psychotherapy

SSD-TS: Exploring the Potential of Linear State Space Models for Diffusion Models in Time Series Imputation

Vision Backbone Efficient Selection for Image Classification in Low-Data Regimes

Boolean Matrix Logic Programming on the GPU

Iterative Utility Judgment Framework via LLMs Inspired by Relevance in Philosophy

ITBLS: A Dataset of Interactive Conversations Over Tabular Information

Fusing Echocardiography Images and Medical Records for Continuous Patient Stratification

Joint Problems in Learning Multiple Dynamical Systems

Radio Map Estimation: Empirical Validation and Analysis

"I see models being a whole other thing": An Empirical Study of Pre-Trained Model Naming Conventions and A Tool for Enhancing Naming Consistency

LEGO: Learning and Graph-Optimized Modular Tracker for Online Multi-Object Tracking with Point Clouds

Adaptation and Optimization of Automatic Speech Recognition (ASR) for the Maritime Domain in the Field of VHF Communication

PC-Sampler: Position-Aware Calibration of Decoding Bias in Masked Diffusion Models

Modeling Relational Logic Circuits for And-Inverter Graph Convolutional Network

FutureX: An Advanced Live Benchmark for LLM Agents in Future Prediction

Scaling Up without Fading Out: Goal-Aware Sparse GNN for RL-based Generalized Planning

DualSG: A Dual-Stream Explicit Semantic-Guided Multivariate Time Series Forecasting Framework

Modeling Uncertainty: Constraint-Based Belief States in Imperfect-Information Games

Data-Efficient Safe Policy Improvement Using Parametric Structure

Towards Urban Planning AI Agent in the Age of Agentic AI

Dispositions and Roles of Generically Dependent Entities

Efficient Network Automatic Relevance Determination

Modeling the Diachronic Evolution of Legal Norms: An LRMoo-Based, Component-Level Approach

Language-Guided Multi-Agent Learning in Simulations: A Unified Framework and Evaluation

It's the Thought that Counts: Evaluating the Attempts of Frontier LLMs to Persuade on Harmful Topics

Hierarchical Reinforcement Learning in Multi-Goal Spatial Navigation with Autonomous Mobile Robots

Trust, but verify

Two Heads are Better Than One: Test-time Scaling of Multi-agent Collaborative Reasoning

Hawkeye:Efficient Reasoning with Model Collaboration

GoAI: Enhancing AI Students' Learning Paths and Idea Generation via Graph of AI Ideas

The StudyChat Dataset: Student Dialogues With ChatGPT in an Artificial Intelligence Course

VRoPE: Rotary Position Embedding for Video Large Language Models

Where to Go Next Day: Multi-scale Spatial-Temporal Decoupled Model for Mid-term Human Mobility Prediction

LEGO-GraphRAG: Modularizing Graph-based Retrieval-Augmented Generation for Design Space Exploration

GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation

Unintended Misalignment from Agentic Fine-Tuning: Risks and Mitigation

Ask Good Questions for Large Language Models

Efficient Knowledge Graph Unlearning with Zeroth-order Information

Evaluating Identity Leakage in Speaker De-Identification Systems

ASDFormer: A Transformer with Mixtures of Pooling-Classifier Experts for Robust Autism Diagnosis and Biomarker Discovery

Embodied-R1: Reinforced Embodied Reasoning for General Robotic Manipulation

Chunks as Arms: Multi-Armed Bandit-Guided Sampling for Long-Context LLM Preference Optimization

Setup Once, Secure Always: A Single-Setup Secure Federated Learning Aggregation Protocol with Forward and Backward Secrecy for Dynamic Users

Created by

Haebom

Author

Nazatul Haque Sultan, Yan Bo, Yansong Gao, Seyit Camtepe, Arash Mahboubi, Hang Thanh Bui, Aufeef Chauhan, Hamed Aboutorab, Michael Bewong, Dineshkumar Singh, Praveen Gauravaram, Rafiqul Islam, Sharif Abuadbba

Outline

This paper proposes a novel secure aggregation protocol for federated learning (FL), a privacy-sensitive application, that enables multiple users to collaboratively train machine learning models without sharing raw data. To overcome the limitations of existing single-configuration protocols, which do not support dynamic user participation and provide strong privacy (forward and backward secrecy), the proposed protocol requires only a single setup operation for the entire FL training, allowing new users to join or leave at any time. It efficiently masks updates without user communication using lightweight symmetric homomorphic encryption and key negation techniques, and protects against model mismatch attacks with a simple verification mechanism using message authentication codes (MACs). Our protocol is the first to combine forward and backward secrecy, dropout resilience, and model integrity verification in a single-configuration design. We present an end-to-end prototype implementation and source code, along with a formal security proof. Experimental results demonstrate that our protocol reduces user-side computation by approximately 99% compared to existing state-of-the-art protocols (e-SeaFL), making it highly practical for real-world FL deployments, especially on resource-constrained devices.

Takeaways, Limitations

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Takeaways:

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Supports full FL learning with a single setup, significantly reducing communication and computation overhead.

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Allows dynamic user engagement (join and leave).

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Provides forward and reverse security to achieve strong privacy.

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Introducing a model integrity verification mechanism using a message authentication code (MAC).

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Improved practicality by reducing user-side computational load by 99% compared to existing cutting-edge protocols.

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Provides formal security proofs and open source prototypes.

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Limitations:

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Limitations, specifically mentioned in the paper, is not presented. Further experiments and analysis may be required to further evaluate its performance and security.

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