Cover Image

Impact of Network Topology on Byzantine Resilience in Decentralized Federated Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Bhattacharya, Siddhartha, Helo, Daniel, Siegel, Joshua
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Distributed, Parallel, and Cluster Computing I.2.11; C.4; C.2.4
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

How Machine Learning-Data Driven Replication Strategies Enhance Fault Tolerance in Large-Scale Distributed Systems
by: Murimi, Almond Kiruthu
Published: (2025)

HFedATM: Hierarchical Federated Domain Generalization via Optimal Transport and Regularized Mean Aggregation
by: Nguyen, Thinh, et al.
Published: (2025)

Federated Learning Model Aggregation in Heterogenous Aerial and Space Networks
by: Dong, Fan, et al.
Published: (2023)

FLEdge: Benchmarking Federated Machine Learning Applications in Edge Computing Systems
by: Woisetschläger, Herbert, et al.
Published: (2023)

Accelerating Geo-distributed Machine Learning with Network-Aware Adaptive Tree and Auxiliary Route
by: Li, Zonghang, et al.
Published: (2024)

AMP4EC: Adaptive Model Partitioning Framework for Efficient Deep Learning Inference in Edge Computing Environments
by: Zhang, Guilin, et al.
Published: (2025)

Federated Fine-Tuning of LLMs on the Very Edge: The Good, the Bad, the Ugly
by: Woisetschläger, Herbert, et al.
Published: (2023)

TAGC: Optimizing Gradient Communication in Distributed Transformer Training
by: Polyakov, Igor, et al.
Published: (2025)

A Taxonomy and Resolution Strategy for Client-Level Disagreements in Federated Learning
by: Rosendal, Daan, et al.
Published: (2026)

Bridging Generalization Gap of Heterogeneous Federated Clients Using Generative Models
by: Niu, Ziru, et al.
Published: (2025)

Federated Few-Shot Learning on Neuromorphic Hardware: An Empirical Study Across Physical Edge Nodes
by: Motta, Steven, et al.
Published: (2026)

Scheduling the Unschedulable: Taming Black-Box LLM Inference at Scale
by: Yuan, Renzhong, et al.
Published: (2026)

FedStrategist: A Meta-Learning Framework for Adaptive and Robust Aggregation in Federated Learning
by: Haque, Md Rafid, et al.
Published: (2025)

A Framework for testing Federated Learning algorithms using an edge-like environment
by: Schwanck, Felipe Machado, et al.
Published: (2024)

Adaptive GPU Resource Allocation for Multi-Agent Collaborative Reasoning in Serverless Environments
by: Zhang, Guilin, et al.
Published: (2025)

AutoDDL: Automatic Distributed Deep Learning with Near-Optimal Bandwidth Cost
by: Chen, Jinfan, et al.
Published: (2023)

TokenCake: A KV-Cache-centric Serving Framework for LLM-based Multi-Agent Applications
by: Bian, Zhuohang, et al.
Published: (2025)

Token Coherence: Adapting MESI Cache Protocols to Minimize Synchronization Overhead in Multi-Agent LLM Systems
by: Parakhin, Vladyslav
Published: (2026)

Mobile Traffic Prediction at the Edge Through Distributed and Deep Transfer Learning
by: Petrella, Alfredo, et al.
Published: (2023)

SparkAttention: High-Performance Multi-Head Attention for Large Models on Volta GPU Architecture
by: Xu, Youxuan, et al.
Published: (2025)

Chimera: Efficiently Training Large-Scale Neural Networks with Bidirectional Pipelines
by: Li, Shigang, et al.
Published: (2021)

Near-Optimal Sparse Allreduce for Distributed Deep Learning
by: Li, Shigang, et al.
Published: (2022)

How Can We Train Deep Learning Models Across Clouds and Continents? An Experimental Study
by: Erben, Alexander, et al.
Published: (2023)

Complex Event Processing in the Edge: A Combined Optimization Approach for Data and Code Placement
by: Uyanık, Halit, et al.
Published: (2026)

FlashSparse: Minimizing Computation Redundancy for Fast Sparse Matrix Multiplications on Tensor Cores
by: Shi, Jinliang, et al.
Published: (2024)

Learning to Collaborate: An Orchestrated-Decentralized Framework for Peer-to-Peer LLM Federation
by: Singh, Inderjeet, et al.
Published: (2026)

Agent Identity URI Scheme: Topology-Independent Naming and Capability-Based Discovery for Multi-Agent Systems
by: Rodriguez Jr, Roland R.
Published: (2026)

Designing Scalable Rate Limiting Systems: Algorithms, Architecture, and Distributed Solutions
by: Guan, Bo
Published: (2026)

Asynchronous Byzantine Federated Learning
by: Cox, Bart, et al.
Published: (2024)

Libra: Unleashing GPU Heterogeneity for High-Performance Sparse Matrix Multiplication
by: Shi, Jinliang, et al.
Published: (2025)

LayerKV: Optimizing Large Language Model Serving with Layer-wise KV Cache Management
by: Xiong, Yi, et al.
Published: (2024)

Towards Message Brokers for Generative AI: Survey, Challenges, and Opportunities
by: Saleh, Alaa, et al.
Published: (2023)

Decentralized Task Scheduling in Distributed Systems: A Deep Reinforcement Learning Approach
by: John, Daniel Benniah
Published: (2026)

Training LLMs on HPC Systems: Best Practices from the OpenGPT-X Project
by: Penke, Carolin, et al.
Published: (2025)

The Bureaucracy of Speed: Structural Equivalence Between Memory Consistency Models and Multi-Agent Authorization Revocation
by: Parakhin, Vladyslav
Published: (2026)

Byzantine-Resilient Federated Learning via QUBO-Based Client Selection on Quantum Annealers
by: Ferenczi, Andras, et al.
Published: (2026)

From Detection to Recovery: Operational Analysis on LLM Pre-training with 504 GPUs
by: Kang, Daemyung, et al.
Published: (2026)

Autonomous Edge-Deployed AI Agents for Electric Vehicle Charging Infrastructure Management
by: Cherifi, Mohammed
Published: (2026)

An Empirical Study of the Impact of Federated Learning on Machine Learning Model Accuracy
by: Yang, Haotian, et al.
Published: (2025)

Shipwright: Proving liveness of distributed systems with Byzantine participants
by: Leung, Derek, et al.
Published: (2025)