35 research outputs found
Over-the-air Federated Policy Gradient
In recent years, over-the-air aggregation has been widely considered in
large-scale distributed learning, optimization, and sensing. In this paper, we
propose the over-the-air federated policy gradient algorithm, where all agents
simultaneously broadcast an analog signal carrying local information to a
common wireless channel, and a central controller uses the received aggregated
waveform to update the policy parameters. We investigate the effect of noise
and channel distortion on the convergence of the proposed algorithm, and
establish the complexities of communication and sampling for finding an
-approximate stationary point. Finally, we present some simulation
results to show the effectiveness of the algorithm
A Differential Private Method for Distributed Optimization in Directed Networks via State Decomposition
In this paper, we study the problem of consensus-based distributed
optimization where a network of agents, abstracted as a directed graph, aims to
minimize the sum of all agents' cost functions collaboratively. In existing
distributed optimization approaches (Push-Pull/AB) for directed graphs, all
agents exchange their states with neighbors to achieve the optimal solution
with a constant stepsize, which may lead to the disclosure of sensitive and
private information. For privacy preservation, we propose a novel
state-decomposition based gradient tracking approach (SD-Push-Pull) for
distributed optimzation over directed networks that preserves differential
privacy, which is a strong notion that protects agents' privacy against an
adversary with arbitrary auxiliary information. The main idea of the proposed
approach is to decompose the gradient state of each agent into two sub-states.
Only one substate is exchanged by the agent with its neighbours over time, and
the other one is kept private. That is to say, only one substate is visible to
an adversary, protecting the privacy from being leaked. It is proved that under
certain decomposition principles, a bound for the sub-optimality of the
proposed algorithm can be derived and the differential privacy is achieved
simultaneously. Moreover, the trade-off between differential privacy and the
optimization accuracy is also characterized. Finally, a numerical simulation is
provided to illustrate the effectiveness of the proposed approach
LQG Control Over SWIPT-enabled Wireless Communication Network
In this paper, we consider using simultaneous wireless information and power
transfer (SWIPT) to recharge the sensor in the LQG control, which provides a
new approach to prolonging the network lifetime. We analyze the stability of
the proposed system model and show that there exist two critical values for the
power splitting ratio {\alpha}. Then, we propose an optimization problem to
derive the optimal value of {\alpha}. This problem is non-convex but its
numerical solution can be derived by our proposed algorithm efficiently.
Moreover, we provide the feasible condition of the proposed optimization
problem. Finally, simulation results are presented to verify and illustrate the
main theoretical results
Intention prediction-based control for vehicle platoon to handle driver cut-In
Vehicle platoons (VPs) are groups of vehicles driving together with a short inter-vehicle gap and a harmonized velocity. For a long period, the VPs and human-driven vehicles (HDVs) will coexist in mixed traffic flow, where the cut-in maneuver of the HDVs towards the VPs can be frequently expected. In this paper, to handle such cut-ins, we propose an intention prediction-based control method for the VPs by considering the tradeoff between the platoon integrity and traffic safety. Particularly, the proposed method is designed to prevent as many cut-ins as possible while taking care of the road safety. It consists of a cut-in prediction part, including intention and trajectory prediction algorithms, and a finite state machine (FSM)-based predictive control part, including a high-level FSM and a low-level predictive control. Driver-in-the-loop experiments were conducted in the VP-based driving scenarios to train the intention prediction algorithm and test the proposed method. We show the results detailing the control behavior of the proposed method in a no cut-in test, a mandatory cut-in test, and three discretionary cut-in tests. The results demonstrate that the proposed method can predict the cut-in intention of human drivers in real time. Besides, according to the prediction results, the proposed method can prevent cut-ins for the VPs while taking care of the road safety.Agency for Science, Technology and Research (A*STAR)Submitted/Accepted versionThis work was supported by A∗STAR under its RIE2020 Advanced Manufacturing and Engineering (AME) Industry Alignment Fund C PrePositioning (IAF-PP) under Award A19D6a0053