22 papers · ranked by Valyu relevance
Zhiyuan Zhai, Xiaojun Yuan, Wei Ni, Xin Wang + 2 more
—While distributed learning offers a new learning paradigm for distributed network with no central coordination, it is constrained by communication bottleneck between nodes. We develop a new event-triggered gossip framework for distributed learning to reduce inter-node communication overhead. The framework introduces…
Zhi Chen, Yadan Luo, Zi Huang, Jingjing Li + 2 more
—In this paper, we propose a Distributed Zero-Shot Learning (DistZSL) framework that can fully exploit decentralized data to learn an effective model for unseen classes. Considering the data heterogeneity issues across distributed nodes, we introduce two key components to ensure the effective learning of DistZSL: a…
Shamik Bhattacharyya, Rachel Kalpana Kalaimani
—Federated learning is a privacy-focused approach towards machine learning where models are trained on client devices with locally available data and aggregated at a central server. However, the dependence on a single central server is challenging in the case of a large number of clients and even poses the risk of a…
Pengchao Han, Xi Huang, Yi Fang, Guojun Han
—Collaborative learning has emerged as a key paradigm in large-scale intelligent systems, enabling distributed agents to cooperatively train their models while addressing their privacy concerns. Central to this paradigm is knowledge distillation (KD), a technique that facilitates efficient knowledge transfer among…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
Kasra Borazjani, Naji Khosravan, Rajeev Sahay, Bita Akram + 1 more
Multi-modal multi-task (M3T) foundation models (FMs) have recently shown transformative potential in artificial intelligence, with emerging applications in education. However, their deployment in real-world educational settings is hindered by privacy regulations, data silos, and limited domain-specific data…
Mohamed Amine Legheraba, Stefan Galkiewicz, Maria Gradinariu Potop-Butucaru, Sébastien Tixeuil
Decentralized learning enhances privacy, scalability, and fault tolerance by distributing data and computation across nodes. A popular approach is Federated learning, which relies on a central aggregator, yet faces challenges such as server vulnerabilities, scalability issues, privacy risks and most importantly, the…
Xuwei Tan, Tian Xie, Xue Zheng, Aylin Yener + 5 more
Federated learning (FL) is a distributed learning paradigm that facilitates training a global machine-learning model without collecting the raw data from distributed clients. Recent advances in FL have addressed several considerations that are likely to transpire in realistic settings, such as data distribution…
Julie Y. L. Chow, Hilary J. Don, Ben Colagiuri, Evan J. Livesey + 1 more
Associative learning models have traditionally simplified contingency learning by relying on binary classification of cues and outcomes, such as administering a medical treatment (or not) and observing whether the patient recovered (or not). While successful in capturing fundamental learning phenomena across human and…
Qi Zhou, Yantao Yu, Jingxiao Ma, Mohammad S. Obaidat + 4 more
In practical deployments of decentralized federated learning (FL) in Internet of Things (IoT) environments, the non-independent and identically distributed (Non-IID) nature of client-local data limits model performance. Furthermore, concept drift further exacerbates complexity and introduces temporal uncertainty that…
Ziqin Chen, Zuang Wang, Yongqiang Wang
Decentralized optimization enables multiple devices to learn a global machine learning model while each individual device only has access to its local dataset. By avoiding the need for training data to leave individual users' devices, it enhances privacy and scalability compared to conventional centralized learning…
Tommaso Gosetti di Sturmeck, Sebastiano Bergamo, Valentina Mastrorilli, Annarita Patrizi + 5 more
A substantial body of research indicates that spaced training, characterized by longer inter-trial intervals between training epochs, consistently outperforms massed training in promoting durable memory. To investigate the neural mechanisms underlying this difference, we quantified c-Fos expression across 126 brain…
He Zhao, Jingwei Li, Patrick Seeling
Distributed edge sensing systems, such as IoT monitoring nodes, wearable devices, and camera-based sensing terminals, continuously generate privacy-sensitive data that are costly to transmit to a central server. Federated learning (FL) provides a promising solution for collaborative model training without raw-data…
Fleming C. Peck, Hongjing Lu, Jesse Rissman
Humans readily extract statistical regularities from experience, yet natural environments require flexible adaptation when associative structures shift across changing contexts, often without warning. Across two experiments, we show that humans can incidentally learn overlapping and conflicting visual associations even…
Zhang Wei, Pan Rongjun, Wang Shijie, Chen Meiqing
This paper presents an innovative intelligent decision optimization model that integrates distributed blockchain technology with federated reinforcement learning to address critical challenges in ship traffic collaborative supervision. Traditional maritime traffic monitoring systems suffer from data silos, privacy…
Authors not listed
Next Generation Risk Assessment (NGRA) promotes animal-free, exposure-informed, and hypothesis-driven approaches to chemical safety assessment. In silico tools, such as quantitative structure-activity relationship (QSAR) models, are valuable new approach methodologies (NAMs) for use in NGRA. However, the practical…
Authors not listed
Recent advances in machine learning force fields (MLFF) have significantly extended the reach of atomistic simulations. Continuous progress in this field requires reliable reference datasets, accurate MLFF architectures, and efficient active learning strategies to enable robust modeling of complex molecular and…
Tianyong Xu, Peng Tao, Jiali Mu, Xiao Han + 9 more
How does the learning brain give rise to emergent mental operations that enable skill internalization and generalization? Using five-year longitudinal tracking of children acquiring abacus-based mental calculation, we reveal how sustained practice progressively reconfigures whole-brain states. Learning induces…
Authors not listed
This comprehensive review examines the evolution of autonomous materials synthesis laboratories that integrate artificial intelligence with advanced robotics to accelerate discovery. Traditional materials development pipelines typically require 10-20 years, but self-driving laboratories (SDLs) and Materials…
Christopher Earl, Gozde Unal, Hananel Hazan, Samuel A Neymotin
Animals must often navigate environments where feedback about progress toward a goal is sparse or delayed, requiring internal representations of space and memory of prior experience. The hippocampal-entorhinal system is believed to support this capability through distributed spatial representations that guide…
Max Taylor-Davies
Social learning is widely understood as offering a mechanism to mitigate the costs and risks of individual trial-and-error exploration. This cost-avoidance account implies a framing of social learning as a resource-rational adaptation, which should be most beneficial to populations with limited capacity to learn…
Authors not listed
Equivariant graph neural networks have shown remarkable success in molecular property prediction, but their performance on novel molecular geometries remains limited without extensive training data. We present a computationally efficient approach to cross-geometry pretraining for molecular systems that improves…