Privacy-Preserving Distributed Zeroth-Order Optimization

Gratton, Cristiano; Venkategowda, Naveen K. D.; Arablouei, Reza; Werner, Stefan

doi:10.48550/arxiv.2008.13468

Cited by 3 publications

(2 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…• Developing new privacy-preserving techniques for federated learning. This could include exploring new forms of differential privacy, such as zeroth-order [51] or R'enyi differential privacy [52], or developing new methods for composing different forms of privacy to achieve stronger guarantees.…”

Section: Future Workmentioning

confidence: 99%

Privacy-Preserving Federated Learning Model for Healthcare Data

Islam

Ghasemi

Mohammed

2022

2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC)

View full text Add to dashboard Cite

Federated Learning (FL) is a method for training machine learning algorithms on decentralized data where sharing the raw data is not feasible due to privacy regulations. An instance of such data is Electronic Health Records (EHRs), which contain confidential patient information. In FL, the sensitive data is not shared, rather local models are trained and the model parameters are then aggregated on a central server. However, this method presents privacy challenges, necessitating the implementation of privacy protection strategies, such as data anonymization, before sharing the model parameters. Balancing the trade-off between privacy and utility is a crucial aspect in FL research, as integrating privacy algorithms can have an impact on the utility. The objective of this thesis is to improve the performance of FL while maintaining privacy, through techniques like data generalization, feature selection for dimension reduction, and minimizing noise in the anonymization process. This research also investigates separating data based on features instead of records and evaluates the performance of the proposed model using real healthcare data, with the aim of developing a predictive model for healthcare applications.

show abstract

Section: Future Workmentioning

confidence: 99%

Privacy-Preserving Federated Learning Model for Healthcare Data

Islam

Ghasemi

Mohammed

2022

2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC)

View full text Add to dashboard Cite

show abstract

“…Moreover, under different zeroth-order oracles, we show that our learning framework always exhibits a reduced variance of the estimated gradient compared with GVF-based policy evaluation. Note that most of the existing distributed ZOO algorithms [30][31][32][33] essentially evaluate policies via GVFs. In [34,35],…”

Section: Introductionmentioning

confidence: 99%

Distributed Multi-Agent Reinforcement Learning Based on Graph-Induced Local Value-Functions

Jing¹,

He²,

George³

et al. 2022

Preprint

View full text Add to dashboard Cite

The main challenge of large-scale cooperative multi-agent reinforcement learning (MARL) is twofold: (i) the RL algorithm is desired to be distributed due to limited resource for each individual agent; (ii) issues on convergence or computational complexity emerge due to the curse of dimensionality. Unfortunately, most of existing distributed RL references only focus on ensuring that the individual policy-seeking process of each agent is based on local information, but fail to solve the scalability issue induced by high dimensions of the state and action spaces when facing large-scale networks. In this paper, we propose a general distributed framework for cooperative MARL by utilizing the structures of graphs involved in this problem. We introduce three graphs in MARL, namely, the coordination graph, the observation graph and the reward graph. Based on these three graphs, and a given communication graph, we propose two distributed RL approaches. The first approach utilizes the inherent decomposability property of the problem itself, whose efficiency depends on the structures of the aforementioned four graphs, and is able to produce a high performance under specific graphical conditions. The second approach provides an approximate solution and is applicable for any graphs. Here the approximation error depends on an artificially designed index. The choice of this index is a trade-off between minimizing the approximation error and reducing the computational complexity. Simulations show that our RL algorithms have a significantly improved scalability to large-scale MASs compared with centralized and consensus-based distributed RL algorithms.

show abstract

Asynchronous Distributed Reinforcement Learning for LQR Control via Zeroth-Order Block Coordinate Descent

Jing¹,

Bai²,

George³

et al. 2021

Preprint

View full text Add to dashboard Cite

Recently introduced distributed zeroth-order optimization (ZOO) algorithms have shown their utility in distributed reinforcement learning (RL). Unfortunately, in the gradient estimation process, almost all of them require random samples with the same dimension as the global variable and/or require evaluation of the global cost function, which may induce high estimation variance for large-scale networks. In this paper, we propose a novel distributed zeroth-order algorithm by leveraging the network structure inherent in the optimization objective, which allows each agent to estimate its local gradient by local cost evaluation independently, without use of any consensus protocol. The proposed algorithm exhibits an asynchronous update scheme, and is designed for stochastic non-convex optimization with a possibly non-convex feasible domain based on the block coordinate descent method. The algorithm is later employed as a distributed model-free RL algorithm for distributed linear quadratic regulator design, where a learning graph is designed to describe the required interaction relationship among agents in distributed learning. We provide an empirical validation of the proposed algorithm to benchmark its performance on convergence rate and variance against a centralized ZOO algorithm.

show abstract

Privacy-Preserving Distributed Zeroth-Order Optimization

Cited by 3 publications

References 38 publications

Privacy-Preserving Federated Learning Model for Healthcare Data

Privacy-Preserving Federated Learning Model for Healthcare Data

Distributed Multi-Agent Reinforcement Learning Based on Graph-Induced Local Value-Functions

Asynchronous Distributed Reinforcement Learning for LQR Control via Zeroth-Order Block Coordinate Descent

Contact Info

Product

Resources

About