conftrace_

distributed machine learning

24 papers

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Also known as

DISTRIBUTED ML DML

Co-occurring keywords

distributed learning (563) parameter server (14) stochastic gradient descent (1091) byzantine fault tolerance (27) communication efficiency (297) adversarial attack (1621) convergence rate (607) robust aggregation (15) byzantine resilience (21) distributed training (188)

Papers

AdaGK-SGD: Adaptive Global Knowledge Guided Distributed Stochastic Gradient Descent AAAI 2025
On the Robustness of Distributed Machine Learning Against Transfer Attacks AAAI 2025
Incorporating Serverless Computing into P2P Networks for ML Training: In-Database Tasks and Their Scalability Implications (Student Abstract) AAAI 2024
Seeing the Forest through the Trees: Data Leakage from Partial Transformer Gradients EMNLP 2024
HyperPrism: An Adaptive Non-linear Aggregation Framework for Distributed Machine Learning over Non-IID Data and Time-varying Communication Links NIPS 2024
Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML NIPS 2024
Auto-Differentiation of Relational Computations for Very Large Scale Machine Learning ICML 2023
On the Privacy-Robustness-Utility Trilemma in Distributed Learning ICML 2023
Collie: Finding Performance Anomalies in RDMA Subsystems NSDI 2022
EasyASR: A Distributed Machine Learning Platform for End-to-end Automatic Speech Recognition AAAI 2021
Scaling Distributed Machine Learning with In-Network Aggregation NSDI 2021
KungFu: Making Training in Distributed Machine Learning Adaptive OSDI 2020
GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning JMLR 2020
Distributed Machine Learning through Heterogeneous Edge Systems AAAI 2020
Fall of Empires: Breaking Byzantine-tolerant SGD by Inner Product Manipulation UAI 2019
Learning to Learn Gradient Aggregation by Gradient Descent IJCAI 2019
BML: A High-performance, Low-cost Gradient Synchronization Algorithm for DML Training NIPS 2018
Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent NIPS 2017
MLlib: Machine Learning in Apache Spark JMLR 2016
Mixed Robust/Average Submodular Partitioning: Fast Algorithms, Guarantees, and Applications NIPS 2015
On Model Parallelization and Scheduling Strategies for Distributed Machine Learning NIPS 2014
Communication Efficient Distributed Machine Learning with the Parameter Server NIPS 2014
More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server NIPS 2013
Optimistic Concurrency Control for Distributed Unsupervised Learning NIPS 2013