1,721,703 research outputs found
An Ant colony system for transport user equilibrium analysis in congested networks
No full textIn this paper we present Ant Colony System for Traffic Assignment (ACS-TA)
for the solution of deterministic and stochastic user equilibria (DUE and SUE, respectively)
problems. DUE and SUE are two well known transportation problems where the transportation
demand has to be assigned to an underlying network (supply in transportation terminology)
according to single user satisfaction rather than aiming at some global optimum.
ACS-TA turns the classic ACS meta-heuristic for discrete optimization into a technique for
equilibrium computation. ACS-TA can be easily adapted to take into account all aspects
characterizing the traffic assignment problem: multiple origin-destination pairs, link congestion,
non-separable cost link functions, elasticity of demand, multiple classes of demand
and different user cost models including stochastic cost perception. Applications to different
networks, including a non-separable costs case study and the standard Sioux Falls benchmark,
are reported. Results show good performance and wider applicability with respect to
conventional approaches especially for stochastic user equilibrium computation
Federated Knowledge Recycling: Privacy-preserving synthetic data sharing
No full textFederated learning has emerged as a paradigm for collaborative learning, enabling the development of robust models without the need to centralise sensitive data. However, conventional federated learning techniques have privacy and security vulnerabilities due to the exposure of models, parameters or updates, which can be exploited as an attack surface. This paper presents Federated Knowledge Recycling (FedKR), a cross-silo federated learning approach that uses locally generated synthetic data to facilitate collaboration between institutions. FedKR combines advanced data generation techniques with a dynamic aggregation process to provide greater security against privacy attacks than existing methods, significantly reducing the attack surface. Experimental results on generic and medical datasets show that FedKR achieves competitive performance, with an average improvement in accuracy of 4.24% compared to training models from local data, demonstrating particular effectiveness in data scarcity scenarios
An application of ant colony systems for DUE and SUE assignment in congested transportation networks
No full text
Synthetic image learning: Preserving performance and preventing Membership Inference Attacks
No full textGenerative artificial intelligence has transformed the generation of synthetic data, providing innovative solutions to challenges like data scarcity and privacy, which are particularly critical infields such as medicine. However, the effective use of this synthetic data to train high-performance models remains a significant challenge. This paper addresses this issue by introducing Knowledge Recycling (KR), a pipeline designed to optimise the generation and use of synthetic data for training downstream classifiers. At the heart of this pipeline is Generative Knowledge Distillation, the proposed technique that significantly improves the quality and usefulness of the information provided to classifiers through a synthetic dataset regeneration and soft labelling mechanism. The KR pipeline has been tested on a variety of datasets, with a focus on six highly heterogeneous medical image datasets, ranging from retinal images to organ scans. The results show a significant reduction in the performance gap between models trained on real and synthetic data, with models based on synthetic data outperforming those trained on real data in some cases. Furthermore, the resulting models show almost complete immunity to Membership Inference Attacks, manifesting privacy properties missing in models trained with conventional techniques
Federated Survival Forests
Full text linkSurvival analysis is a subfield of statistics concerned with modeling the occurrence time of a particular event of interest for a population. Survival analysis found widespread applications in healthcare, engineering, and social sciences. However, real-world applications involve survival datasets that are distributed, incomplete, censored, and confidential. In this context, federated learning can tremendously improve the performance of survival analysis applications. Federated learning provides a set of privacy-preserving techniques to jointly train machine learning models on multiple datasets without compromising user privacy, leading to a better generalization performance. However, despite the widespread development of federated learning in recent AI research, few studies focus on federated survival analysis. In this work, we present a novel federated algorithm for survival analysis based on one of the most successful survival models, the random survival forest. We call the proposed method Federated Survival Forest (FedSurF). With a single communication round, FedSurF obtains a discriminative power comparable to deep-learning-based federated models trained over hundreds of federated iterations. Moreover, FedSurF retains all the advantages of random forests, namely low computational cost and natural handling of missing values and incomplete datasets. These advantages are especially desirable in real-world federated environments with multiple small datasets stored on devices with low computational capabilities. Numerical experiments compare FedSurF with state-of-the-art survival models in federated networks, showing how FedSurF outperforms deep-learning-based federated algorithms in realistic environments with non-identically distributed data
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