Decentralized online convex optimization with compressed communications
Due to the iterative information exchange between agents, decentralized multi-agent
optimization algorithms often incur large communication overhead, which is not affordable in …
optimization algorithms often incur large communication overhead, which is not affordable in …
Federated Learning from Heterogeneous Data via Controlled Air Aggregation with Bayesian Estimation
Federated learning (FL) is an emerging machine learning paradigm for training models
across multiple edge devices holding local data sets, without explicitly exchanging the data …
across multiple edge devices holding local data sets, without explicitly exchanging the data …
Zero-regret performative prediction under inequality constraints
Performative prediction is a recently proposed framework where predictions guide decision-
making and hence influence future data distributions. Such performative phenomena are …
making and hence influence future data distributions. Such performative phenomena are …
Joint online optimization of model training and analog aggregation for wireless edge learning
We consider federated learning in a wireless edge network, where multiple power-limited
mobile devices collaboratively train a global model, using their local data with the assistance …
mobile devices collaboratively train a global model, using their local data with the assistance …
Federated learning from heterogeneous data via controlled Bayesian air aggregation
Federated learning (FL) is an emerging machine learning paradigm for training models
across multiple edge devices holding local data sets, without explicitly exchanging the data …
across multiple edge devices holding local data sets, without explicitly exchanging the data …
Decentralized Stochastic Optimization With Pairwise Constraints and Variance Reduction
This paper focuses on minimizing the decentralized finite-sum optimization over a network,
where each pair of neighboring agents is associated with a nonlinear proximity constraint …
where each pair of neighboring agents is associated with a nonlinear proximity constraint …
CoBAAF: Controlled Bayesian air aggregation federated learning from heterogeneous data
Federated learning (FL) is an emerging machine learning paradigm for training models
across multiple edge devices holding local data sets, without explicitly exchanging the data …
across multiple edge devices holding local data sets, without explicitly exchanging the data …
Distributed Event-Triggered Bandit Convex Optimization with Time-Varying Constraints
K Zhang, X Yi, G Wen, M Cao, KH Johansson… - arXiv preprint arXiv …, 2024 - arxiv.org
This paper considers the distributed bandit convex optimization problem with time-varying
inequality constraints over a network of agents, where the goal is to minimize network regret …
inequality constraints over a network of agents, where the goal is to minimize network regret …
Federated Linear Bandit Learning via Over-the-air Computation
In this paper, we investigate federated contextual linear bandit learning within a wireless
system that comprises a server and multiple devices. Each device interacts with the …
system that comprises a server and multiple devices. Each device interacts with the …
Improved Dynamic Regret of Distributed Online Multiple Frank-Wolfe Convex Optimization
W Zhang, Y Shi, B Zhang, D Yuan - arXiv preprint arXiv:2305.12957, 2023 - arxiv.org
In this paper, we consider a distributed online convex optimization problem over a time-
varying multi-agent network. The goal of this network is to minimize a global loss function …
varying multi-agent network. The goal of this network is to minimize a global loss function …