Search Results for author:
Found 15 papers, 3 papers with code
TernaryVote: Differentially Private, Communication Efficient, and Byzantine Resilient Distributed Optimization on Heterogeneous Data
In this paper, we propose TernaryVote, which combines a ternary compressor and the majority vote mechanism to realize differential privacy, gradient compression, and Byzantine resilience simultaneously.
Hierarchical Federated Learning in Wireless Networks: Pruning Tackles Bandwidth Scarcity and System Heterogeneity
While a practical wireless network has many tiers where end users do not directly communicate with the central server, the users' devices have limited computation and battery powers, and the serving base station (BS) has a fixed bandwidth.
Distributed Learning over Networks with Graph-Attention-Based Personalization
In conventional distributed learning over a network, multiple agents collaboratively build a common machine learning model.
Magnitude Matters: Fixing SIGNSGD Through Magnitude-Aware Sparsification in the Presence of Data Heterogeneity
Communication overhead has become one of the major bottlenecks in the distributed training of deep neural networks.
Breaking the Communication-Privacy-Accuracy Tradeoff with $f$-Differential Privacy
We consider a federated data analytics problem in which a server coordinates the collaborative data analysis of multiple users with privacy concerns and limited communication capability.
Resource Constrained Vehicular Edge Federated Learning with Highly Mobile Connected Vehicles
This paper proposes a vehicular edge federated learning (VEFL) solution, where an edge server leverages highly mobile connected vehicles' (CVs') onboard central processing units (CPUs) and local datasets to train a global model.
Mobile MIMO Channel Prediction with ODE-RNN: a Physics-Inspired Adaptive Approach
Through exploring the intrinsic correlation among a set of historical CSI instances randomly obtained in a certain communication environment, channel prediction can significantly increase CSI accuracy and save signaling overhead.
Gradient Obfuscation Gives a False Sense of Security in Federated Learning
Prior work has shown that the gradient sharing strategies in federated learning can be vulnerable to data reconstruction attacks.
Neural Tangent Kernel Empowered Federated Learning
The paradigm addresses the challenge of statistical heterogeneity by transmitting update data that are more expressive than those of the conventional FL paradigms.
Federated Learning via Plurality Vote
Federated learning allows collaborative workers to solve a machine learning problem while preserving data privacy.
Communication-Efficient Federated Learning via Predictive Coding
In each communication round, we select the predictor and quantizer based on the rate-distortion cost, and further reduce the redundancy with entropy coding.
Communication Efficient Federated Learning with Energy Awareness over Wireless Networks
Moreover, most of the existing works assume Channel State Information (CSI) available at both the mobile devices and the parameter server, and thus the mobile devices can adopt fixed transmission rates dictated by the channel capacity.
Stochastic-Sign SGD for Federated Learning with Theoretical Guarantees
We present Stochastic-Sign SGD which utilizes novel stochastic-sign based gradient compressors enabling the aforementioned properties in a unified framework.
Distributed Byzantine Tolerant Stochastic Gradient Descent in the Era of Big Data
The recent advances in sensor technologies and smart devices enable the collaborative collection of a sheer volume of data from multiple information sources.
Decentralized Differentially Private Without-Replacement Stochastic Gradient Descent
While machine learning has achieved remarkable results in a wide variety of domains, the training of models often requires large datasets that may need to be collected from different individuals.
