659 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
Blockchain-Enabled Federated Learning Approach for Vehicular Networks
Data from interconnected vehicles may contain sensitive information such as
location, driving behavior, personal identifiers, etc. Without adequate
safeguards, sharing this data jeopardizes data privacy and system security. The
current centralized data-sharing paradigm in these systems raises particular
concerns about data privacy. Recognizing these challenges, the shift towards
decentralized interactions in technology, as echoed by the principles of
Industry 5.0, becomes paramount. This work is closely aligned with these
principles, emphasizing decentralized, human-centric, and secure technological
interactions in an interconnected vehicular ecosystem. To embody this, we
propose a practical approach that merges two emerging technologies: Federated
Learning (FL) and Blockchain. The integration of these technologies enables the
creation of a decentralized vehicular network. In this setting, vehicles can
learn from each other without compromising privacy while also ensuring data
integrity and accountability. Initial experiments show that compared to
conventional decentralized federated learning techniques, our proposed approach
significantly enhances the performance and security of vehicular networks. The
system's accuracy stands at 91.92\%. While this may appear to be low in
comparison to state-of-the-art federated learning models, our work is
noteworthy because, unlike others, it was achieved in a malicious vehicle
setting. Despite the challenging environment, our method maintains high
accuracy, making it a competent solution for preserving data privacy in
vehicular networks.Comment: 7 page
Trustworthy Federated Learning: A Survey
Federated Learning (FL) has emerged as a significant advancement in the field
of Artificial Intelligence (AI), enabling collaborative model training across
distributed devices while maintaining data privacy. As the importance of FL
increases, addressing trustworthiness issues in its various aspects becomes
crucial. In this survey, we provide an extensive overview of the current state
of Trustworthy FL, exploring existing solutions and well-defined pillars
relevant to Trustworthy . Despite the growth in literature on trustworthy
centralized Machine Learning (ML)/Deep Learning (DL), further efforts are
necessary to identify trustworthiness pillars and evaluation metrics specific
to FL models, as well as to develop solutions for computing trustworthiness
levels. We propose a taxonomy that encompasses three main pillars:
Interpretability, Fairness, and Security & Privacy. Each pillar represents a
dimension of trust, further broken down into different notions. Our survey
covers trustworthiness challenges at every level in FL settings. We present a
comprehensive architecture of Trustworthy FL, addressing the fundamental
principles underlying the concept, and offer an in-depth analysis of trust
assessment mechanisms. In conclusion, we identify key research challenges
related to every aspect of Trustworthy FL and suggest future research
directions. This comprehensive survey serves as a valuable resource for
researchers and practitioners working on the development and implementation of
Trustworthy FL systems, contributing to a more secure and reliable AI
landscape.Comment: 45 Pages, 8 Figures, 9 Table
Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices
Home appliance manufacturers strive to obtain feedback from users to improve
their products and services to build a smart home system. To help manufacturers
develop a smart home system, we design a federated learning (FL) system
leveraging the reputation mechanism to assist home appliance manufacturers to
train a machine learning model based on customers' data. Then, manufacturers
can predict customers' requirements and consumption behaviors in the future.
The working flow of the system includes two stages: in the first stage,
customers train the initial model provided by the manufacturer using both the
mobile phone and the mobile edge computing (MEC) server. Customers collect data
from various home appliances using phones, and then they download and train the
initial model with their local data. After deriving local models, customers
sign on their models and send them to the blockchain. In case customers or
manufacturers are malicious, we use the blockchain to replace the centralized
aggregator in the traditional FL system. Since records on the blockchain are
untampered, malicious customers or manufacturers' activities are traceable. In
the second stage, manufacturers select customers or organizations as miners for
calculating the averaged model using received models from customers. By the end
of the crowdsourcing task, one of the miners, who is selected as the temporary
leader, uploads the model to the blockchain. To protect customers' privacy and
improve the test accuracy, we enforce differential privacy on the extracted
features and propose a new normalization technique. We experimentally
demonstrate that our normalization technique outperforms batch normalization
when features are under differential privacy protection. In addition, to
attract more customers to participate in the crowdsourcing FL task, we design
an incentive mechanism to award participants.Comment: This paper appears in IEEE Internet of Things Journal (IoT-J
iDML: Incentivized Decentralized Machine Learning
With the rising emergence of decentralized and opportunistic approaches to
machine learning, end devices are increasingly tasked with training deep
learning models on-devices using crowd-sourced data that they collect
themselves. These approaches are desirable from a resource consumption
perspective and also from a privacy preservation perspective. When the devices
benefit directly from the trained models, the incentives are implicit -
contributing devices' resources are incentivized by the availability of the
higher-accuracy model that results from collaboration. However, explicit
incentive mechanisms must be provided when end-user devices are asked to
contribute their resources (e.g., computation, communication, and data) to a
task performed primarily for the benefit of others, e.g., training a model for
a task that a neighbor device needs but the device owner is uninterested in. In
this project, we propose a novel blockchain-based incentive mechanism for
completely decentralized and opportunistic learning architectures. We leverage
a smart contract not only for providing explicit incentives to end devices to
participate in decentralized learning but also to create a fully decentralized
mechanism to inspect and reflect on the behavior of the learning architecture
Blockchain-Enabled Federated Learning: A Reference Architecture Design, Implementation, and Verification
This paper presents an innovative reference architecture for
blockchain-enabled federated learning (BCFL), a state-of-the-art approach that
amalgamates the strengths of federated learning and blockchain technology. This
results in a decentralized, collaborative machine learning system that respects
data privacy and user-controlled identity. Our architecture strategically
employs a decentralized identifier (DID)-based authentication system, allowing
participants to authenticate and then gain access to the federated learning
platform securely using their self-sovereign DIDs, which are recorded on the
blockchain. Ensuring robust security and efficient decentralization through the
execution of smart contracts is a key aspect of our approach. Moreover, our
BCFL reference architecture provides significant extensibility, accommodating
the integration of various additional elements, as per specific requirements
and use cases, thereby rendering it an adaptable solution for a wide range of
BCFL applications. Participants can authenticate and then gain access to the
federated learning platform securely using their self-sovereign DIDs, which are
securely recorded on the blockchain. The pivotal contribution of this study is
the successful implementation and validation of a realistic BCFL reference
architecture, marking a significant milestone in the field. We intend to make
the source code publicly accessible shortly, fostering further advancements and
adaptations within the community. This research not only bridges a crucial gap
in the current literature but also lays a solid foundation for future
explorations in the realm of BCFL.Comment: 14 pages, 15 figures, 3 table
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