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Found 15 papers, 0 papers with code
DaringFed: A Dynamic Bayesian Persuasion Pricing for Online Federated Learning under Two-sided Incomplete Information
To incentivize clients to participate in training by offering dynamic rewards to each arriving client, we design a novel Dynamic Bayesian persuasion pricing for online Federated learning (DaringFed) under TII.
One-Point Sampling for Distributed Bandit Convex Optimization with Time-Varying Constraints
Moreover, a reduced static network regret bound $\mathcal{O}( {{T^{2/3 + 4g /3}}} )$ is established for strongly convex local loss functions.
Decentralized Nonconvex Composite Federated Learning with Gradient Tracking and Momentum
Employing DFL methods to solve such general optimization problems leads to the formulation of Decentralized Nonconvex Composite Federated Learning (DNCFL), a topic that remains largely underexplored.
FedCanon: Non-Convex Composite Federated Learning with Efficient Proximal Operation on Heterogeneous Data
To overcome these limitations, we propose a novel composite federated learning algorithm called \textbf{FedCanon}, designed to solve the optimization problems comprising a possibly non-convex loss function and a weakly convex, potentially non-smooth regularization term.
Distributed Nash Equilibrium Seeking in Coalition Games for Uncertain Euler-Lagrange Systems With Application to USV Swarm Confrontation
In the coalition game, each agent collaborates with other agents within the same coalition to optimize its coalition's cost function while simultaneously competing against agents in other coalitions.
Optimal Parameter Adaptation for Safety-Critical Control via Safe Barrier Bayesian Optimization
In this work, we shed light on the crucial role of configurable parameters in the CBF method for performance enhancement with a systematical categorization.
Designing Consensus-Based Distributed Filtering over Directed Graphs
The transient performance is also analyzed with the fusion step tending to infinity.
Performance Analysis of Distributed Filtering under Mismatched Noise Covariances
Additionally, we prove that the estimation error covariance of the consensus-based distributed filter under mismatched noise covariances can be bounded by the Frobenius norms of the noise covariance deviations and the trace of the nominal performance evaluation index.
Distributed Fractional Bayesian Learning for Adaptive Optimization
This paper considers a distributed adaptive optimization problem, where all agents only have access to their local cost functions with a common unknown parameter, whereas they mean to collaboratively estimate the true parameter and find the optimal solution over a connected network.
Data-driven Dynamic Event-triggered Control
This paper revisits the event-triggered control problem from a data-driven perspective, where unknown continuous-time linear systems subject to disturbances are taken into account.
A Novel Dynamic Event-triggered Mechanism for Dynamic Average Consensus
This paper studies a challenging issue introduced in a recent survey, namely designing a distributed event-based scheme to solve the dynamic average consensus (DAC) problem.
A Framework on Fully Distributed State Estimation and Cooperative Stabilization of LTI Plants
Further, the proposed method can be applied to pure fully distributed state estimation scenarios and modified for noise-bounded LTI plants.
Cooperative Control of Multi-Channel Linear Systems with Self-Organizing Private Agents
Cooperative behavior design for multi-agent systems with collective tasks is a critical issue in promoting swarm intelligence.
Data-Driven Deep Learning Based Hybrid Beamforming for Aerial Massive MIMO-OFDM Systems with Implicit CSI
To this end, by modeling the key transmission modules as an end-to-end (E2E) neural network, this paper proposes a data-driven deep learning (DL)-based unified hybrid beamforming framework for both the time division duplex (TDD) and frequency division duplex (FDD) systems with implicit channel state information (CSI).
Multi-Task Variational Information Bottleneck
Multi-task learning (MTL) is an important subject in machine learning and artificial intelligence.
