scholarly journals CrowdSFL: A Secure Crowd Computing Framework Based on Blockchain and Federated Learning

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 773 ◽  
Author(s):  
Ziyuan Li ◽  
Jian Liu ◽  
Jialu Hao ◽  
Huimei Wang ◽  
Ming Xian
Keyword(s):  
Large Scale ◽  
Single Point ◽  
Ddos Attack ◽  
Similar Work ◽  
Peer Network ◽  
Other Information ◽  
Local Storage ◽  
And Storage ◽  
Peer To Peer Network ◽  
Distributed Machine Learning

Over the years, the flourish of crowd computing has enabled enterprises to accomplish computing tasks through crowdsourcing in a large-scale and high-quality manner, and therefore how to efficiently and securely implement crowd computing becomes a hotspot. Some recent work innovatively adopted a P2P (peer-to-peer) network as the communication environment of crowdsourcing. Based on its decentralized control, issues like single-point-of-failure or DDoS attack can be overcome to some extent, but the huge computing capacity and storage costs required by this scheme is always unbearable. Federated learning is a distributed machine learning that supports local storage of data, and clients implement training through interactive gradient values. In our work, we combine blockchain with federated learning and propose a crowdsourcing framework named CrowdSFL, that users can implement crowdsourcing with less overhead and higher security. In addition, to protect the privacy of participants, we design a new re-encryption algorithm based on Elgamal to ensure that interactive values and other information will not be exposed to other participants outside the workflow. Finally, we have proved through experiments that our framework is superior to some similar work in accuracy, efficiency, and overhead.

scholarly journals Unobtrusive monitoring: Statistical dissemination latency estimation in Bitcoin’s peer-to-peer network

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243475
Author(s):  
David Mödinger ◽  
Jan-Hendrik Lorenz ◽  
Rens W. van der Heijden ◽  
Franz J. Hauck
Keyword(s):  
Bayesian Model ◽  
Large Scale ◽  
Risk Estimation ◽  
Peer To Peer ◽  
Third Party ◽  
Network Resources ◽  
Connection Number ◽  
Peer Network ◽  
Time Required ◽  
Peer To Peer Network

The cryptocurrency system Bitcoin uses a peer-to-peer network to distribute new transactions to all participants. For risk estimation and usability aspects of Bitcoin applications, it is necessary to know the time required to disseminate a transaction within the network. Unfortunately, this time is not immediately obvious and hard to acquire. Measuring the dissemination latency requires many connections into the Bitcoin network, wasting network resources. Some third parties operate that way and publish large scale measurements. Relying on these measurements introduces a dependency and requires additional trust. This work describes how to unobtrusively acquire reliable estimates of the dissemination latencies for transactions without involving a third party. The dissemination latency is modelled with a lognormal distribution, and we estimate their parameters using a Bayesian model that can be updated dynamically. Our approach provides reliable estimates even when using only eight connections, the minimum connection number used by the default Bitcoin client. We provide an implementation of our approach as well as datasets for modelling and evaluation. Our approach, while slightly underestimating the latency distribution, is largely congruent with observed dissemination latencies.

Dynamic Load Rebalancing Algorithm for Private Cloud

2014 ◽  
Vol 573 ◽  
pp. 556-559
Author(s):  
A. Shenbaga Bharatha Priya ◽  
J. Ganesh ◽  
Mareeswari M. Devi
Keyword(s):  
Dynamic Load ◽  
Large Scale ◽  
File System ◽  
Single Point ◽  
File Systems ◽  
Distributed File System ◽  
Private Cloud ◽  
Global Knowledge ◽  
Load Imbalance ◽  
And Storage

Infrastructure-As-A-Service (IAAS) provides an environmental setup under any type of cloud. In Distributed file system (DFS), nodes are simultaneously serve computing and storage functions; that is parallel Data Processing and storage in cloud. Here, file is considered as a data or load. That file is partitioned into a number of File chunks (FC) allocated in distinct nodes so that Map Reduce tasks can be performed in parallel over the nodes. Files and Nodes can be dynamically created, deleted, and added. This results in load imbalance in a distributed file system; that is, the file chunks are not distributed as uniformly as possible among the Chunk Servers (CS). Emerging distributed file systems in production systems strongly depend on a central node for chunk reallocation or Distributed node to maintain global knowledge of all chunks. This dependence is clearly inadequate in a large-scale, failure-prone environment because the central load balancer is put under considerable workload that is linearly scaled with the system size, it may thus become the performance bottleneck and the single point of failure and memory wastage in distributed nodes. So, we have to enhance the Client side module with server side module to create, delete and update the file chunks in Client Module. And manage the overall private cloud and apply dynamic load balancing algorithm to perform auto scaling options in private cloud. In this project, a fully distributed load rebalancing algorithm is presented to cope with the load imbalance problem.

Scalable Checkpointing-Based Rollback Recovery Protocol for Geographically Distributed Systems

2012 ◽  
Vol 263-266 ◽  
pp. 1492-1496
Author(s):  
Jin Ho Ahn
Keyword(s):  
Large Scale ◽  
Rollback Recovery ◽  
Core Networks ◽  
Peer Network ◽  
Underlying Network ◽  
Geographically Distributed ◽  
Synchronization Overhead ◽  
High Scalability ◽  
Coordinated Checkpointing ◽  
Peer To Peer Network

Two opposite approaches were proposed to address some scalability problem resulting from coordinated checkpointing's synchronization during failure-free operation: minimizing the number of checkpointing participants and having the checkpointing process non-blocking. However, these previous approaches, oblivious to the underlying network, may not fundamentally provide any breakthrough for ensuring high scalability required in very large-scale P2P-based systems. This paper proposes a non-blocking coordinated checkpointing protocol to significantly reduce checkpointing synchronization overhead by structuring the peer-to-peer network into a set of groups according to a particular criterion. In this protocol, among processes in a group, one is designated as representative with the following special roles, intra-group and inter-group checkpointing coordination. Intra-group checkpointing coordination addresses the checkpointing procedure among processes within a group. On the other hand, inter-group checkpointing coordination is performed only among representatives. Thanks to this beneficial feature, the proposed protocol may considerably reduce the number of checkpointing control messages routed on core networks compared with the existing ones.

scholarly journals A Measurement Study of the Structured Overlay Network in P2P File-Sharing Systems

2007 ◽  
Vol 2007 ◽  
pp. 1-9 ◽  
Author(s):  
Mo Zhou ◽  
Yafei Dai ◽  
Xiaoming Li
Keyword(s):  
Large Scale ◽  
Single Point ◽  
Overlay Network ◽  
File Sharing ◽  
Measurement Study ◽  
P2p Systems ◽  
Ddos Attack ◽  
P2p File Sharing ◽  
Structured Overlay ◽  
Single User

The architecture of P2P file-sharing applications has been developing to meet the needs of large scale demands. The structured overlay network, also known as DHT, has been used in these applications to improve the scalability, and robustness of the system, and to make it free from single-point failure. We believe that the measurement study of the overlay network used in the real file-sharing P2P systems can provide guidance for the designing of such systems, and improve the performance of the system. In this paper, we perform the measurement in two different aspects. First, a modified client is designed to provide view to the overlay network from a single-user vision. Second, the instances of crawler programs deployed in many nodes managed to crawl the user information of the overlay network as much as possible. We also find a vulnerability in the overlay network, combined with the character of the DNS service, a more serious DDoS attack can be launched.

scholarly journals Federated Learning using Peer-to-peer Network for Decentralized Orchestration of Model Weights

2021 ◽  
Author(s):  
Monik Raj Behera ◽  
sudhir upadhyay ◽  
Suresh Shetty ◽  
Robert Otter
Keyword(s):  
Machine Learning ◽  
Single Point ◽  
Peer To Peer ◽  
Transient Time ◽  
Learning Framework ◽  
Peer Network ◽  
Cryptographic Keys ◽  
Aggregated Model ◽  
Generic Design ◽  
Peer To Peer Network

<div>In recent times, Machine learning and Artificial intelligence have become one of the key emerging fields of computer science. Many researchers and businesses are benefited by machine learning models that are trained by data processing at scale. However, machine learning, and particularly Deep Learning requires large amounts of data, that in several instances are proprietary and confidential to many businesses. In order to respect individual organization’s privacy in collaborative machine learning, federated learning could play a crucial role. Such implementations of privacy preserving federated learning find applicability in various ecosystems like finance, health care, legal, research and other fields that require preservation of privacy. However, many such implementations are driven by a centralized architecture in the network, where the aggregator node becomes the single point of failure, and is also expected with lots of computing resources at its disposal. In this paper, we propose an approach of implementing a decentralized, peer-topeer federated learning framework, that leverages RAFT based aggregator selection. The proposal hinges on that fact that there is no one permanent aggregator, but instead a transient, time based elected leader, which will aggregate the models from all the peers in the network. The leader ( aggregator) publishes the aggregated model on the network, for everyone to consume. Along with peer-to-peer network and RAFT based aggregator selection, the framework uses dynamic generation of cryptographic keys, to create a more secure mechanism for delivery of models within the network. The key rotation also ensures anonymity of the sender on the network too. Experiments conducted in the paper, verifies the usage of peer-to-peer network for creating a resilient federated learning network. Although the proposed solution uses an artificial neural network in it’s reference implementation, the generic design of the framework can accommodate any federated learning model within the network.</div>

scholarly journals A study of Storage Area Networks and issues in its management

2021 ◽  
Vol 23 (06) ◽  
pp. 238-245
Author(s):  
Varsha Kulkarni ◽  
◽  
Dr. Nagaraj Bhat ◽  
Keyword(s):  
Data Center ◽  
Large Scale ◽  
Virtual Machines ◽  
Single Point ◽  
Storage System ◽  
Area Network ◽  
Storage Area Network ◽  
Storage Devices ◽  
Storage Area ◽  
Local Storage

A data center has hundreds of servers and storage devices running on virtual machines that can be deployed and migrated over servers as per the requirement. If each server uses local storage, migration of this storage and restoration is mandatory. An attempt to organize and track storage throughout the data center is quite tedious. Using a dedicated storage system like a storage array, it possible to collectively monitor and manage such a network. A storage area network is essentially a network dedicated to storage devices. A storage area network can interconnect devices in all its layers, therefore improving storage availability. Interconnecting all elements in SAN also reduces the chances of a single point of failure. Using the storage devices collectively improves their utilization. SAN offers to manage and maintain all devices in the network. Although SAN is beneficial, it has drawbacks when configuring, monitoring, and managing components in a large-scale network. This paper consolidates the problems associated with SAN and offers possible solutions to overcome them.

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