981 research outputs found
Vertical Federated Learning
Vertical Federated Learning (VFL) is a federated learning setting where
multiple parties with different features about the same set of users jointly
train machine learning models without exposing their raw data or model
parameters. Motivated by the rapid growth in VFL research and real-world
applications, we provide a comprehensive review of the concept and algorithms
of VFL, as well as current advances and challenges in various aspects,
including effectiveness, efficiency, and privacy. We provide an exhaustive
categorization for VFL settings and privacy-preserving protocols and
comprehensively analyze the privacy attacks and defense strategies for each
protocol. In the end, we propose a unified framework, termed VFLow, which
considers the VFL problem under communication, computation, privacy, and
effectiveness constraints. Finally, we review the most recent advances in
industrial applications, highlighting open challenges and future directions for
VFL
Efficiency in the multinational federal republic
The paper is divided in three sections. In the first section, I question the use of the statist redistributive paradigm in federalism. In the second section, I argue that efficiency is a moral principle and that it has a strong normative appeal, especially in contexts of diversity. I show that adopting efficiency as a guiding principle to think of the role of the state, especially in contexts of pluralism, as in MNF, allows us to consider the division of competences in a way that is yet unexplored in political philosophy. Furthermore, I argue that embracing efficiency allows us to avoid the moral problems that other moral approaches encounter, especially as I will defend a non-utilitarian conception of efficiency. That also allows me to show that if one opts for the view that pictures federalism as an efficiency maximizing enterprise, it does not lead to a libertarian conception of federalism. Finally, I try to briefly sketch a possible connection between the principle of efficiency and republican ideal of ‘non-domination’ (Pettit 2012). More specifically I suggest that the pursuit of ‘non-domination’ is totally compatible with the pursuit of efficiency in MNF. In other words, the federal government can interfere to resolve government failures at the sub-unit level, for instance externalities, without being or becoming a dominating agent. The ideal of non-domination supports the sort of strong government interventions defended by egalitarians without having to compromise on the autonomy of federated entities. The combination of efficiency and non-domination ends with a defense of asymmetrical federal arrangements, without sacrificing the equality that states ought to preserve
The changing federated relationship between local and regional cooperatives
The evolution of the federated relationship between local and regional cooperatives is examined from the perspective of local cooperatives’ need for commodity-based farm supplies and regional cooperatives’ identity as food companies. Because locals want many competing bids for the supplies they purchase, they resist a strong and close affiliation with regional cooperatives, which then find themselves with excess capacity. Regionals have responded by instituting tighter bonds with selected local cooperatives operating as "internal supply networks," in exchange for certain benefits. This adaptation reduces the impact of divergent goals among regionals and locals within the federated system.Cooperatives, federation, networks, competition, regionalization., Agribusiness,
BadVFL: Backdoor Attacks in Vertical Federated Learning
Federated learning (FL) enables multiple parties to collaboratively train a
machine learning model without sharing their data; rather, they train their own
model locally and send updates to a central server for aggregation. Depending
on how the data is distributed among the participants, FL can be classified
into Horizontal (HFL) and Vertical (VFL). In VFL, the participants share the
same set of training instances but only host a different and non-overlapping
subset of the whole feature space. Whereas in HFL, each participant shares the
same set of features while the training set is split into locally owned
training data subsets.
VFL is increasingly used in applications like financial fraud detection;
nonetheless, very little work has analyzed its security. In this paper, we
focus on robustness in VFL, in particular, on backdoor attacks, whereby an
adversary attempts to manipulate the aggregate model during the training
process to trigger misclassifications. Performing backdoor attacks in VFL is
more challenging than in HFL, as the adversary i) does not have access to the
labels during training and ii) cannot change the labels as she only has access
to the feature embeddings. We present a first-of-its-kind clean-label backdoor
attack in VFL, which consists of two phases: a label inference and a backdoor
phase. We demonstrate the effectiveness of the attack on three different
datasets, investigate the factors involved in its success, and discuss
countermeasures to mitigate its impact
Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challenges
Federated learning plays an important role in the process of smart cities.
With the development of big data and artificial intelligence, there is a
problem of data privacy protection in this process. Federated learning is
capable of solving this problem. This paper starts with the current
developments of federated learning and its applications in various fields. We
conduct a comprehensive investigation. This paper summarize the latest research
on the application of federated learning in various fields of smart cities.
In-depth understanding of the current development of federated learning from
the Internet of Things, transportation, communications, finance, medical and
other fields. Before that, we introduce the background, definition and key
technologies of federated learning. Further more, we review the key
technologies and the latest results. Finally, we discuss the future
applications and research directions of federated learning in smart cities
Locally Differentially Private Embedding Models in Distributed Fraud Prevention Systems
Global financial crime activity is driving demand for machine learning
solutions in fraud prevention. However, prevention systems are commonly
serviced to financial institutions in isolation, and few provisions exist for
data sharing due to fears of unintentional leaks and adversarial attacks.
Collaborative learning advances in finance are rare, and it is hard to find
real-world insights derived from privacy-preserving data processing systems. In
this paper, we present a collaborative deep learning framework for fraud
prevention, designed from a privacy standpoint, and awarded at the recent PETs
Prize Challenges. We leverage latent embedded representations of varied-length
transaction sequences, along with local differential privacy, in order to
construct a data release mechanism which can securely inform externally hosted
fraud and anomaly detection models. We assess our contribution on two
distributed data sets donated by large payment networks, and demonstrate
robustness to popular inference-time attacks, along with utility-privacy
trade-offs analogous to published work in alternative application domains
Starlit: Privacy-Preserving Federated Learning to Enhance Financial Fraud Detection
Federated Learning (FL) is a data-minimization approach enabling
collaborative model training across diverse clients with local data, avoiding
direct data exchange. However, state-of-the-art FL solutions to identify
fraudulent financial transactions exhibit a subset of the following
limitations. They (1) lack a formal security definition and proof, (2) assume
prior freezing of suspicious customers' accounts by financial institutions
(limiting the solutions' adoption), (3) scale poorly, involving either
computationally expensive modular exponentiation (where is the total number
of financial institutions) or highly inefficient fully homomorphic encryption,
(4) assume the parties have already completed the identity alignment phase,
hence excluding it from the implementation, performance evaluation, and
security analysis, and (5) struggle to resist clients' dropouts. This work
introduces Starlit, a novel scalable privacy-preserving FL mechanism that
overcomes these limitations. It has various applications, such as enhancing
financial fraud detection, mitigating terrorism, and enhancing digital health.
We implemented Starlit and conducted a thorough performance analysis using
synthetic data from a key player in global financial transactions. The
evaluation indicates Starlit's scalability, efficiency, and accuracy
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