Federated Reinforcement Learning with Adaptive Training Times for Edge Caching

To relieve the backhaul link stress and reduce the content acquisition delay,mobile edge caching has become one of the promising approaches.In this paper,a novel federated reinforcement learning(FRL)method with adaptive training times is proposed for edge caching.Through a new federated learning process with the asynchronous model training process and synchronous global aggregation process,the proposed FRL-based edge caching algorithm mitigates the performance degradation brought by the non-identically and independently distributed(noni.i.d.)characteristics of content popularity among edge nodes.The theoretical bound of the loss function difference is analyzed in the paper,based on which the training times adaption mechanism is proposed to deal with the tradeoff between local training and global aggregation for each edge node in the federation.Numerical simulations have verified that the proposed FRL-based edge caching method outperforms other baseline methods in terms of the caching benefit,the cache hit ratio and the convergence speed.

面向物联网的激励感知区块链辅助的智能边缘缓存与计算迁移研究

The rapid development of artificial intelligence has pushed the Internet of Things(Io T)into a new stage.Facing with the explosive growth of data and the higher quality of service required by users,edge computing and caching are regarded as promising solutions.However,the resources in edge nodes(ENs)are not inexhaustible.In this paper,we propose an incentive-aware blockchain-assisted intelligent edge caching and computation offloading scheme for Io T,which is dedicated to providing a secure and intelligent solution for collaborative ENs in resource optimization and controls.Specifically,we jointly optimize offloading and caching decisions as well as computing and communication resources allocation to minimize the total cost for tasks completion in the EN.Furthermore,a blockchain incentive and contribution co-aware federated deep reinforcement learning algorithm is designed to solve this optimization problem.In this algorithm,we construct an incentive-aware blockchain-assisted collaboration mechanism which operates during local training,with the aim to strengthen the willingness of ENs to participate in collaboration with security guarantee.Meanwhile,a contribution-based federated aggregation method is developed,in which the aggregation weights of EN gradients are based on their contributions,thereby improving the training effect.Finally,compared with other baseline schemes,the numerical results prove that our scheme has an efficient optimization utility of resources with significant advantages in total cost reduction and caching performance.

Optimal replication strategy for mitigating burst traffic in information-centric satellite networks: a focus on remote sensing image transmission

Information-centric satellite networks play a crucial role in remote sensing applications,particularly in the transmission of remote sensing images.However,the occurrence of burst traffic poses significant challenges in meeting the increased bandwidth demands.Traditional content delivery networks are ill-equipped to handle such bursts due to their pre-deployed content.In this paper,we propose an optimal replication strategy for mitigating burst traffic in information-centric satellite networks,specifically focusing on the transmission of remote sensing images.Our strategy involves selecting the most optimal replication delivery satellite node when multiple users subscribe to the same remote sensing content within a short time,effectively reducing network transmission data and preventing throughput degradation caused by burst traffic expansion.We formulate the content delivery process as a multi-objective optimization problem and apply Markov decision processes to determine the optimal value for burst traffic reduction.To address these challenges,we leverage federated reinforcement learning techniques.Additionally,we use bloom filters with subdivision and data identification methods to enable rapid retrieval and encoding of remote sensing images.Through software-based simulations using a low Earth orbit satellite constellation,we validate the effectiveness of our proposed strategy,achieving a significant 17%reduction in the average delivery delay.This paper offers valuable insights into efficient content delivery in satellite networks,specifically targeting the transmission of remote sensing images,and presents a promising approach to mitigate burst traffic challenges in information-centric environments.