cs updates on arXiv.org striveceo.github.io

Multi-Agent Meta-Advisor for UAV Fleet Trajectory Design in Vehicular Networks

arxiv.org·12w

Harnessing Implicit Cooperation: A Multi-Agent Reinforcement Learning Approach Towards Decentralized Local Energy Markets

arxiv.org·12w

MARLEM: A Multi-Agent Reinforcement Learning Simulation Framework for Implicit Cooperation in Decentralized Local Energy Markets

arxiv.org·12w

Multi-agent cooperation through in-context co-player inference

arxiv.org·12w·Hacker News

Managing Credible Anonymous Identities in Web 3.0 Services: A Scalable On-Chain Admission Framework with Recursive Proof Aggregation

arxiv.org·12w

push0: Scalable and Fault-Tolerant Orchestration for Zero-Knowledge Proof Generation

arxiv.org·12w

SRFed: Mitigating Poisoning Attacks in Privacy-Preserving Federated Learning with Heterogeneous Data

arxiv.org·12w

Protocol as Poetry: Case Study on Pak's Protocol Arts

arxiv.org·51w

FedEFC: Federated Learning Using Enhanced Forward Correction Against Noisy Labels

arxiv.org·57w

Hiring under Congestion and Algorithmic Monoculture: Value of Strategic Behavior

arxiv.org·63w

Noncooperative Coordination for Decentralized Air Traffic Management

arxiv.org·12w

Beyond Context Sharing: A Unified Agent Communication Protocol (ACP) for Secure, Federated, and Autonomous Agent-to-Agent (A2A) Orchestration

arxiv.org·12w

Orchestration-Free Customer Service Automation: A Privacy-Preserving and Flowchart-Guided Framework

arxiv.org·12w

Privacy-Preserving and Secure Spectrum Sharing for Database-Driven Cognitive Radio Networks

arxiv.org·12w

MEV in Binance Builder

arxiv.org·12w

Revisiting Backdoor Threat in Federated Instruction Tuning from a Signal Aggregation Perspective

arxiv.org·12w

Exploiting Layer-Specific Vulnerabilities to Backdoor Attack in Federated Learning

arxiv.org·12w

Deep Ignorance: Filtering Pretraining Data Builds Tamper-Resistant Safeguards into Open-Weight LLMs

arxiv.org·39w

FedX: Explanation-Guided Pruning for Communication-Efficient Federated Learning in Remote Sensing

arxiv.org·39w

From Few to Many Faults: Adaptive Byzantine Agreement with Optimal Communication

arxiv.org·50w

Log in to enable infinite scrolling