3,396 research outputs found
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 and Robustness in Federated Learning: Attacks and Defenses
As data are increasingly being stored in different silos and societies
becoming more aware of data privacy issues, the traditional centralized
training of artificial intelligence (AI) models is facing efficiency and
privacy challenges. Recently, federated learning (FL) has emerged as an
alternative solution and continue to thrive in this new reality. Existing FL
protocol design has been shown to be vulnerable to adversaries within or
outside of the system, compromising data privacy and system robustness. Besides
training powerful global models, it is of paramount importance to design FL
systems that have privacy guarantees and are resistant to different types of
adversaries. In this paper, we conduct the first comprehensive survey on this
topic. Through a concise introduction to the concept of FL, and a unique
taxonomy covering: 1) threat models; 2) poisoning attacks and defenses against
robustness; 3) inference attacks and defenses against privacy, we provide an
accessible review of this important topic. We highlight the intuitions, key
techniques as well as fundamental assumptions adopted by various attacks and
defenses. Finally, we discuss promising future research directions towards
robust and privacy-preserving federated learning.Comment: arXiv admin note: text overlap with arXiv:2003.02133; text overlap
with arXiv:1911.11815 by other author
When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions
The intersection of the Foundation Model (FM) and Federated Learning (FL)
provides mutual benefits, presents a unique opportunity to unlock new
possibilities in AI research, and address critical challenges in AI and
real-world applications. FL expands the availability of data for FMs and
enables computation sharing, distributing the training process and reducing the
burden on FL participants. It promotes collaborative FM development,
democratizing the process and fostering inclusivity and innovation. On the
other hand, FM, with its enormous size, pre-trained knowledge, and exceptional
performance, serves as a robust starting point for FL, facilitating faster
convergence and better performance under non-iid data. Additionally, leveraging
FM to generate synthetic data enriches data diversity, reduces overfitting, and
preserves privacy. By examining the interplay between FL and FM, this paper
aims to deepen the understanding of their synergistic relationship,
highlighting the motivations, challenges, and future directions. Through an
exploration of the challenges faced by FL and FM individually and their
interconnections, we aim to inspire future research directions that can further
enhance both fields, driving advancements and propelling the development of
privacy-preserving and scalable AI systems
Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning
Federated learning is a distributed framework for training machine learning
models over the data residing at mobile devices, while protecting the privacy
of individual users. A major bottleneck in scaling federated learning to a
large number of users is the overhead of secure model aggregation across many
users. In particular, the overhead of the state-of-the-art protocols for secure
model aggregation grows quadratically with the number of users. In this paper,
we propose the first secure aggregation framework, named Turbo-Aggregate, that
in a network with users achieves a secure aggregation overhead of
, as opposed to , while tolerating up to a user dropout
rate of . Turbo-Aggregate employs a multi-group circular strategy for
efficient model aggregation, and leverages additive secret sharing and novel
coding techniques for injecting aggregation redundancy in order to handle user
dropouts while guaranteeing user privacy. We experimentally demonstrate that
Turbo-Aggregate achieves a total running time that grows almost linear in the
number of users, and provides up to speedup over the
state-of-the-art protocols with up to users. Our experiments also
demonstrate the impact of model size and bandwidth on the performance of
Turbo-Aggregate
A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability
Graph Neural Networks (GNNs) have made rapid developments in the recent
years. Due to their great ability in modeling graph-structured data, GNNs are
vastly used in various applications, including high-stakes scenarios such as
financial analysis, traffic predictions, and drug discovery. Despite their
great potential in benefiting humans in the real world, recent study shows that
GNNs can leak private information, are vulnerable to adversarial attacks, can
inherit and magnify societal bias from training data and lack interpretability,
which have risk of causing unintentional harm to the users and society. For
example, existing works demonstrate that attackers can fool the GNNs to give
the outcome they desire with unnoticeable perturbation on training graph. GNNs
trained on social networks may embed the discrimination in their decision
process, strengthening the undesirable societal bias. Consequently, trustworthy
GNNs in various aspects are emerging to prevent the harm from GNN models and
increase the users' trust in GNNs. In this paper, we give a comprehensive
survey of GNNs in the computational aspects of privacy, robustness, fairness,
and explainability. For each aspect, we give the taxonomy of the related
methods and formulate the general frameworks for the multiple categories of
trustworthy GNNs. We also discuss the future research directions of each aspect
and connections between these aspects to help achieve trustworthiness
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