CCM-FL:Covert communication mechanisms for federated learning in crowd sensing IoT

The past decades have witnessed a wide application of federated learning in crowd sensing,to handle the numerous data collected by the sensors and provide the users with precise and customized services.Meanwhile,how to protect the private information of users in federated learning has become an important research topic.Compared with the differential privacy(DP)technique and secure multiparty computation(SMC)strategy,the covert communication mechanism in federated learning is more efficient and energy-saving in training the ma-chine learning models.In this paper,we study the covert communication problem for federated learning in crowd sensing Internet-of-Things networks.Different from the previous works about covert communication in federated learning,most of which are considered in a centralized framework and experimental-based,we firstly proposes a centralized covert communication mechanism for federated learning among n learning agents,the time complexity of which is O(log n),approximating to the optimal solution.Secondly,for the federated learning without parameter server,which is a harder case,we show that solving such a problem is NP-hard and prove the existence of a distributed covert communication mechanism with O(log logΔlog n)times,approximating to the optimal solution.Δis the maximum distance between any pair of learning agents.Theoretical analysis and nu-merical simulations are presented to show the performance of our covert communication mechanisms.We hope that our covert communication work can shed some light on how to protect the privacy of federated learning in crowd sensing from the view of communications.

Distributed Weighted Data Aggregation Algorithm in End-to-Edge Communication Networks Based on Multi-armed Bandit

As a combination of edge computing and artificial intelligence,edge intelligence has become a promising technique and provided its users with a series of fast,precise,and customized services.In edge intelligence,when learning agents are deployed on the edge side,the data aggregation from the end side to the designated edge devices is an important research topic.Considering the various importance of end devices,this paper studies the weighted data aggregation problem in a single hop end-to-edge communication network.Firstly,to make sure all the end devices with various weights are fairly treated in data aggregation,a distributed end-to-edge cooperative scheme is proposed.Then,to handle the massive contention on the wireless channel caused by end devices,a multi-armed bandit(MAB)algorithm is designed to help the end devices find their most appropriate update rates.Diffe-rent from the traditional data aggregation works,combining the MAB enables our algorithm a higher efficiency in data aggregation.With a theoretical analysis,we show that the efficiency of our algorithm is asymptotically optimal.Comparative experiments with previous works are also conducted to show the strength of our algorithm.

A survey of fault tolerant consensus in wireless networks

Wireless networks have become integral to modern communication systems,enabling the seamless exchange of information across a myriad of applications.However,the inherent characteristics of wireless channels,such as fading,interference,and openness,pose significant challenges to achieving fault-tolerant consensus within these networks.Fault-tolerant consensus,a critical aspect of distributed systems,ensures that network nodes collectively agree on a consistent value even in the presence of faulty or compromised components.This survey paper provides a comprehensive overview of faulttolerant consensus mechanisms specifically tailored for wireless networks.We explore the diverse range of consensus protocols and techniques that have been developed to address the unique challenges of wireless environments.The paper systematically categorizes these consensus mechanisms based on their underlying principles,communication models,and fault models.It investigates how these mechanisms handle various types of faults,including communication errors,node failures,and malicious attacks.It highlights key use cases,such as sensor networks,Internet of Things(IoT)applications,wireless blockchain,and vehicular networks,where fault-tolerant consensus plays a pivotal role in ensuring reliable and accurate data dissemination.

Optimized Consensus for Blockchain in Internet of Things Networks via Reinforcement Learning

Most blockchain systems currently adopt resource-consuming protocols to achieve consensus between miners;for example,the Proof-of-Work(PoW)and Practical Byzantine Fault Tolerant(PBFT)schemes,which have a high consumption of computing/communication resources and usually require reliable communications with bounded delay.However,these protocols may be unsuitable for Internet of Things(IoT)networks because the IoT devices are usually lightweight,battery-operated,and deployed in an unreliable wireless environment.Therefore,this paper studies an efficient consensus protocol for blockchain in IoT networks via reinforcement learning.Specifically,the consensus protocol in this work is designed on the basis of the Proof-of-Communication(PoC)scheme directly in a single-hop wireless network with unreliable communications.A distributed MultiAgent Reinforcement Learning(MARL)algorithm is proposed to improve the efficiency and fairness of consensus for miners in the blockchain system.In this algorithm,each agent uses a matrix to depict the efficiency and fairness of the recent consensus and tunes its actions and rewards carefully in an actor-critic framework to seek effective performance.Empirical results from the simulation show that the fairness of consensus in the proposed algorithm is guaranteed,and the efficiency nearly reaches a centralized optimal solution.

Trustworthy decentralized collaborative learning for edge intelligence:A survey

Edge intelligence is an emerging technology that enables artificial intelligence on connected systems and devices in close proximity to the data sources.decentralized collaborative learning(DCL)is a novel edge intelligence technique that allows distributed clients to cooperatively train a global learning model without revealing their data.DCL has a wide range of applications in various domains,such as smart city and autonomous driving.However,DCL faces significant challenges in ensuring its trustworthiness,as data isolation and privacy issues make DCL systems vulnerable to adversarial attacks that aim to breach system confidentiality,undermine learning reliability or violate data privacy.Therefore,it is crucial to design DCL in a trustworthy manner,with a focus on security,robustness,and privacy.In this survey,we present a comprehensive review of existing efforts for designing trustworthy DCL systems from the three key aformentioned aspects:security,robustness,and privacy.We analyze the threats that affect the trustworthiness of DCL across different scenarios and assess specific technical solutions for achieving each aspect of trustworthy DCL(TDCL).Finally,we highlight open challenges and future directions for advancing TDCL research and practice.

Reliable Data Storage in Heterogeneous Wireless Sensor Networks by Jointly Optimizing Routing and Storage Node Deployment

In the era of big data,sensor networks have been pervasively deployed,producing a large amount of data for various applications.However,because sensor networks are usually placed in hostile environments,managing the huge volume of data is a very challenging issue.In this study,we mainly focus on the data storage reliability problem in heterogeneous wireless sensor networks where robust storage nodes are deployed in sensor networks and data redundancy is utilized through coding techniques.To minimize data delivery and data storage costs,we design an algorithm to jointly optimize data routing and storage node deployment.The problem can be formulated as a binary nonlinear combinatorial optimization problem,and due to its NP-hardness,designing approximation algorithms is highly nontrivial.By leveraging the Markov approximation framework,we elaborately design an efficient algorithm driven by a continuous-time Markov chain to schedule the deployment of the storage node and corresponding routing strategy.We also perform extensive simulations to verify the efficacy of our algorithm.

Distributed Consensus for Blockchains in Internet-of-Things Networks

In recent years,due to the wide implementation of mobile agents,the Internet-of-Things(IoT) networks have been applied in several real-life scenarios,servicing applications in the areas of public safety,proximity-based services,and fog computing.Meanwhile,when more complex tasks are processed in IoT networks,demands on identity authentication,certifiable traceability,and privacy protection for services in IoT networks increase.Building a blockchain system in IoT networks can greatly satisfy such demands.However,the blockchain building in IoT brings about new challenges compared with that in the traditional full-blown Internet with reliable transmissions,especially in terms of achieving consensus on each block in complex wireless environments,which directly motivates our work.In this study,we fully considered the challenges of achieving a consensus in a blockchain system in IoT networks,including the negative impacts caused by contention and interference in wireless channel,and the lack of reliable transmissions and prior network organizations.By proposing a distributed consensus algorithm for blockchains on multi-hop IoT networks,we showed that it is possible to directly reach a consensus for blockchains in IoT networks,without relying on any additional network layers or protocols to provide reliable and ordered communications.In our theoretical analysis,we showed that our consensus algorithm is asymptotically optimal on time complexity and is energy saving.The extensive simulation results also validate our conclusions in the theoretical analysis.

Implementation of Abstract MAC Layer Under Jamming

In the past decades,with the widespread implementation of wireless networks,such as the Internet of Things,an enormous demand for designing relative algorithms for various realistic scenarios has arisen.However,with the widening of scales and deepening of network layers,it has become increasingly challenging to design such algorithms when the issues of message dissemination at high levels and the contention management at the physical layer are considered.Accordingly,the abstract medium access control(absMAC)layer,which was proposed in2009,is designed to solve this problem.Specifically,the absMAC layer consists of two basic operations for network agents:the acknowledgement operation to broadcast messages to all neighbors and the progress operation to receive messages from neighbors.The absMAC layer divides the wireless algorithm design into two independent and manageable components,i.e.,to implement the absMAC layer over a physical network and to solve higher-level problems based on the acknowledgement and progress operations provided by the absMAC layer,which makes the algorithm design easier and simpler.In this study,we consider the implementation of the absMAC layer under jamming.An efficient algorithm is proposed to implement the absMAC layer,attached with rigorous theoretical analyses and extensive simulation results.Based on the implemented absMAC layer,many high-level algorithms in non-jamming cases can be executed in a jamming network.