arXiv subjects

Dhirendra Shukla

Publications and source records attributed to Dhirendra Shukla.

3 recordsLinked to original sources

arXiv2023

A Communication-efficient Local Differentially Private Algorithm in Federated Optimization

Federated optimization, wherein several agents in a network collaborate with a central server to achieve optimal social cost over the network with no requirement for exchanging information among agents, has attracted significant interest from the research community. In this context, agents demand resources based on their local computation. Due to the exchange of optimization parameters such as states, constraints, or objective functions with a central server, an adversary may infer sensitive information of agents. We develop a differentially-private additive-increase and multiplicative-decrease algorithm to allocate multiple divisible shared heterogeneous resources to agents in a network. The developed algorithm provides a differential privacy guarantee to each agent in the network. The algorithm does not require inter-agent communication, and the agents do not need to share their cost function or their derivatives with other agents or a central server; however, they share their allocation states with a central server that keeps track of the aggregate consumption of resources. The algorithm incurs very little communication overhead; for m heterogeneous resources in the system, the asymptotic upper bound on the communication complexity is O(m) bits at a time step. Furthermore, if the algorithm converges in K time steps, then the upper bound communication complexity will be O(mK) bits. The algorithm can find applications in several areas, including smart cities, smart energy systems, resource management in the sixth generation (6G) wireless networks with privacy guarantees, etc. We present experimental results to check the efficacy of the algorithm. Furthermore, we present empirical analyses for the trade-off between privacy and algorithm efficiency.

cs.MA↗

arXiv2023

Near-optimal Differentially Private Client Selection in Federated Settings

We develop an iterative differentially private algorithm for client selection in federated settings. We consider a federated network wherein clients coordinate with a central server to complete a task; however, the clients decide whether to participate or not at a time step based on their preferences -- local computation and probabilistic intent. The algorithm does not require client-to-client information exchange. The developed algorithm provides near-optimal values to the clients over long-term average participation with a certain differential privacy guarantee. Finally, we present the experimental results to check the algorithm's efficacy.

cs.CR↗

arXiv2022

Optimal Regulation of Prosumers and Consumers in Smart Energy Communities

In smart energy communities, households of a particular geographical location make a cooperative group to achieve the community's social welfare. Prosumers are the users that both consume and produce energy. In this paper, we develop stochastic and distributed algorithms to regulate the number of consumers and the number of prosumers with heterogeneous energy sources in the smart energy community. In the community, each prosumer has one of the heterogeneous energy sources such as solar photovoltaic panels or wind turbines installed in their household. The prosumers and consumers decide in a probabilistic way when to be active. They keep their information private and do not need to share it with other prosumers or consumers in the community. Moreover, we consider a central server that keeps track of the total number of active prosumers and consumers and sends feedback signals in the community at each time step; the prosumers and consumers use these signals to calculate their probabilistic intent. We present experimental results to check the efficacy of the algorithms. We observe that the average number of times prosumers and consumers are active reaches the optimal value over time, and the community asymptotically achieves the social optimum value.

eess.SY↗