963 research outputs found
Towards causal federated learning : a federated approach to learning representations using causal invariance
Federated Learning is an emerging privacy-preserving distributed machine learning approach to building a shared model by performing distributed training locally on participating devices (clients) and aggregating the local models into a global one. As this approach prevents data collection and aggregation, it helps in reducing associated privacy risks to a great extent.
However, the data samples across all participating clients are
usually not independent and identically distributed (non-i.i.d.), and Out of Distribution (OOD) generalization for the learned models can be poor. Besides this challenge, federated learning also remains vulnerable to various attacks on security wherein a few malicious participating entities work towards inserting backdoors, degrading the generated aggregated model as well as inferring the data owned by participating entities. In this work, we propose an approach for learning invariant (causal) features common to all participating clients in a federated learning setup and analyse empirically how it enhances the Out of Distribution (OOD) accuracy as well as the privacy of the final learned model. Although Federated Learning allows for participants to contribute their local data without revealing it, it faces issues in data security and in accurately paying participants for quality data contributions. In this report, we also propose an EOS Blockchain design and workflow to establish data security, a novel validation error based metric upon which we qualify gradient uploads for payment, and implement a small example of our Blockchain Causal Federated Learning model to analyze its performance with respect to robustness, privacy and fairness in incentivization.L’apprentissage fédéré est une approche émergente d’apprentissage automatique distribué
préservant la confidentialité pour créer un modèle partagé en effectuant une formation
distribuée localement sur les appareils participants (clients) et en agrégeant les modèles locaux
en un modèle global. Comme cette approche empêche la collecte et l’agrégation de données,
elle contribue à réduire dans une large mesure les risques associés à la vie privée. Cependant,
les échantillons de données de tous les clients participants sont généralement pas indépendante
et distribuée de manière identique (non-i.i.d.), et la généralisation hors distribution (OOD)
pour les modèles appris peut être médiocre. Outre ce défi, l’apprentissage fédéré reste
également vulnérable à diverses attaques contre la sécurité dans lesquelles quelques entités
participantes malveillantes s’efforcent d’insérer des portes dérobées, dégradant le modèle
agrégé généré ainsi que d’inférer les données détenues par les entités participantes. Dans cet
article, nous proposons une approche pour l’apprentissage des caractéristiques invariantes
(causales) communes à tous les clients participants dans une configuration d’apprentissage
fédérée et analysons empiriquement comment elle améliore la précision hors distribution
(OOD) ainsi que la confidentialité du modèle appris final. Bien que l’apprentissage fédéré
permette aux participants de contribuer leurs données locales sans les révéler, il se heurte à des
problèmes de sécurité des données et de paiement précis des participants pour des contributions
de données de qualité. Dans ce rapport, nous proposons également une conception et un
flux de travail EOS Blockchain pour établir la sécurité des données, une nouvelle métrique
basée sur les erreurs de validation sur laquelle nous qualifions les téléchargements de gradient
pour le paiement, et implémentons un petit exemple de notre modèle d’apprentissage fédéré
blockchain pour analyser ses performances
DIGITAL ASSETS TRANSMISSION BETWEEN ORGANIZATIONS: MUSIC INDUSTRY CASE
This research addresses the following experiences as a contribution to the topic of Blockchain
applications. First, the modeling of a Music Industry revenue distribution problem. Second, the
Integration of Blockchain platforms and Legacy software. Third, the design of an algorithm that solves
the distribution of Digital Assets across organizations within the Music Industry. Ultimately, the
analysis of the Performance of Blockchain platforms (Ethereum and Hyperledger) in terms of Latency
and Throughput. Additionally, the purpose of the research is to show that the modeling of a Music
Industry payment system is possible using Blockchain Technology. Therefore, the old business model
of the Music Industry, which possessed flaws and inefficiencies, could potentially change into a trustless
environment benefiting all the participants y paying their contributions instantaneously. Moreover, the
necessity of a solution is reinforced by an internship experienced in a MITACS project in conjunction
with a company called Membran to design and implement a Blockchain solution that shortens the gap
between Spotify and the payment to the Labels and Artists.
The system distributes value by automatically calculating payments whenever the Digital
Assets (Music Tracks revenue) are imported. The application defines specific roles and variables to
simulate the Music Industry. For example, Distributors as an entry point and Artists at the end of the
chain. Although, any participant within the network can create agreements and benefit from the
distribution.
The implementation of this research took the Hyperledger Composer framework to use the
Hyperledger Fabric Blockchain as the Private Distributed Ledger, and the public Blockchain Ethereum
with the Ganache Client for development purposes. Extensive research of the strengths and weaknesses
of these technologies included the descriptions of features like the consensus algorithms, modular
architectures, and smart contracts.
Ultimately, the performance of these technologies compared Hyperledger Composer and
Ethereum in terms of Latency and Throughput. The conclusion of this research pointed that Hyperledger
Composer with features like the role-based architecture for applications, Programmable ChainCode
(Smart Contracts), and Business Network Definitions, is better suitable for modeling customized
solutions and outperforms Ethereum in terms of performance when testing the same number of
transactions, the same logic of the chain code and the same machine environment
Decentralized Finance (DeFi): A Survey
Decentralized Finance (DeFi) is a new paradigm in the creation, distribution,
and utilization of financial services via the integration of blockchain
technology. Our research conducts a comprehensive introduction and meticulous
classification of various DeFi applications. Beyond that, we thoroughly analyze
these risks from both technical and economic perspectives, spanning multiple
layers. We point out research gaps and revenues, covering technical
advancements, innovative economics, and sociology and ecology optimization
Analysis of Privacy-aware Data Sharing in Cyber-physical Energy Systems
In this thesis, we determine the key factors and correlations among the privacy, security, and utility requirements of grid networks to ensure effective inter-and intra-actions within physical layer equipment (e.g., distributed energy resources (DERs), intelligent electronic devices (IEDs), etc.). We have conducted a comprehensive analysis of the existing consensus mechanisms in blockchain-enabled smart grids while pointing out the potential research gaps. We develop a practical and effective consensus mechanism for a private and permissioned blockchain-enabled Supervisory control and data acquisition (SCADA) system. Moreover, we bridge a common and popular industrial control system (ICS) protocol, distributed network protocol 3 (DNP3) with the blockchain network to ensure smooth operation. In addition, we develop differential privacy (DP)-enabled strategies to achieve data security, privacy, and utility requirements of the power system network under an adversarial setting. Specifically, we aim to analyze and develop a provable correlation between privacy loss and other DP parameters considering the variations of attacks and their impacts along with DP constraints. This will enable modern power grid designers to develop, design, and employ DP-based fault-tolerant models in data-driven power grid operation and control. Furthermore, we conduct feasibility and quality-of-service (QoS) analysis of the DP mechanism and the grid to achieve certified robustness. Feasibility analysis of the privacy measure provides an assessment of the practicability of differential privacy in grid operation and warns the operators about the possible failures and incoming attacks on physical layer operations. QoS is analyzed in the power grid in terms of data accuracy, computational overhead, and resource utilization
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