IPLS : A Framework for Decentralized Federated Learning

Pappas, Christodoulos; Chatzopoulos, Dimitris; Lalis, Spyros; Vavalis, Manolis

doi:10.48550/arxiv.2101.01901

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…To overcome the aforementioned issues, a decentralized approach, where the model relies on the own resources of the node, can be used as an alternative to perform the training. Pappas et al [38] proposed a decentralized FL framework, called, interplanetary learning system, that is inspired by interplanetary file system. The proposed framework allows mobile agents to collaborate in the model's training that doesn't rely on any central server.…”

Section: B Federated Learning Big Data Storagementioning

confidence: 99%

Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directions

Gadekallu,

Pham,

Huynh-The

et al. 2021

Preprint

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Big data has remarkably evolved over the last few years to realize an enormous volume of data generated from newly emerging services and applications and a massive number of Internet-of-Things (IoT) devices. The potential of big data can be realized via analytic and learning techniques, in which the data from various sources is transferred to a central cloud for central storage, processing, and training. However, this conventional approach faces critical issues in terms of data privacy as the data may include sensitive data such as personal information, governments, banking accounts. To overcome this challenge, federated learning (FL) appeared to be a promising learning technique. However, a gap exists in the literature that a comprehensive survey on FL for big data services and applications is yet to be conducted. In this article, we present a survey on the use of FL for big data services and applications, aiming to provide general readers with an overview of FL, big data, and the motivations behind the use of FL for big data. In particular, we extensively review the use of FL for key big data services, including big data acquisition, big data storage, big data analytics, and big data privacy preservation. Subsequently, we review the potential of FL for big data applications, such as smart city, smart healthcare, smart transportation, smart grid, and social media. Further, we summarize a number of important projects on FL-big data and discuss key challenges of this interesting topic along with several promising solutions and directions.

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Section: B Federated Learning Big Data Storagementioning

confidence: 99%

Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directions

Gadekallu,

Pham,

Huynh-The

et al. 2021

Preprint

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“…The framework uses blockchain for the global model storage and the local model update exchange without a central server. In [30], the authors introduce a fully decentralized federated learning framework, termed IPLS, that is partially based on the interplanetary file system (IPFS). By using IPLS and connecting into the corresponding private IPFS network, any party can initiate the training process of a model or join an ongoing training process that has already been started by another party.…”

Section: Decentralized Federated Learning Implementationmentioning

confidence: 99%

DACFL: Dynamic Average Consensus Based Federated Learning in Decentralized Topology

Chen¹,

Li²,

Zhu³

et al. 2021

Preprint

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Federated learning (FL) is a burgeoning distributed machine learning framework where a central parameter server (PS) coordinates many local users to train a globally consistent model. Conventional federated learning inevitably relies on a centralized topology with a PS. As a result, it will paralyze once the PS fails. To alleviate such a single point failure, especially on the PS, some existing work has provided decentralized FL (DFL) implementations like CDSGD and D-PSGD to facilitate FL in a decentralized topology. However, there are still some problems with these methods, e.g., significant divergence between users' final models in CDSGD and a network-wide model average necessity in D-PSGD. In order to solve these deficiency, this paper devises a new DFL implementation coined as DACFL, where each user trains its model using its own training data and exchanges the intermediate models with its neighbors through a symmetric and doubly stochastic matrix. The DACFL treats the progress of each user's local training as a discrete-time process and employs a first order dynamic average consensus (FODAC) method to track the average model in the absence of the PS. In this paper, we also provide a theoretical convergence analysis of DACFL on the premise of i.i.d data to strengthen its rationality. The experimental results on MNIST, Fashion-MNIST and CIFAR-10 validate the feasibility of our solution in both time-invariant and time-varying network topologies, and declare that DACFL outperforms D-PSGD and CDSGD in most cases.

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IPLS : A Framework for Decentralized Federated Learning

Cited by 2 publications

References 11 publications

Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directions

Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directions

DACFL: Dynamic Average Consensus Based Federated Learning in Decentralized Topology

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