Results 61 to 70 of about 294,534 (263)
MAPL: Model Agnostic Peer-to-peer Learning
Effective collaboration among heterogeneous clients in a decentralized setting is a rather unexplored avenue in the literature. To structurally address this, we introduce Model Agnostic Peer-to-peer Learning (coined as MAPL) a novel approach to simultaneously learn heterogeneous personalized models as well as a collaboration graph through peer-to-peer ...
Sayak Mukherjee +2 more
openaire +2 more sources
CEACAM1 participation in breast cancer progression
In invasive breast cancer (BC), CEACAM1 shifts from an apical to a uniform membranous/cytoplasmic pattern, or is lost, as tumors dedifferentiate, inversely tracking the Ki‐67 proliferative index. In MCF‐7 cells, only CEACAM1‐4L suppresses proliferation, repressing cell cycle and growth factor genes.
Mykola Lyndin +3 more
wiley +1 more source
Backdoor Attacks in Peer-to-Peer Federated Learning
Most machine learning applications rely on centralized learning processes, opening up the risk of exposure of their training datasets. While federated learning (FL) mitigates to some extent these privacy risks, it relies on a trusted aggregation server for training a shared global model. Recently, new distributed learning architectures based on Peer-to-
Georgios Syros +4 more
openaire +2 more sources
FedCoin: A Peer-to-Peer Payment System for Federated Learning [PDF]
Federated learning (FL) is an emerging collaborative machine learning method to train models on distributed datasets with privacy concerns. To properly incentivize data owners to contribute their efforts, Shapley Value (SV) is often adopted to fairly assess their contribution. However, the calculation of SV is time-consuming and computationally costly.
Yuan Liu 0002 +5 more
openaire +2 more sources
The cytoskeleton‐mediated transport of mitochondria via tunnelling nanotubes restores respiration, increases ATP production, rescues cells from apoptosis, activates the AKT/mTOR signalling pathway, promotes cell migration and invasiveness, contributes to cancer progression and treatment resistance.
Stanislava Martínková, Jan Trnka
wiley +1 more source
Peer-to-Peer Federated Learning: A Comprehensive Survey
The last five years have seen considerable growth in the topic of peer-to-peer (P2P) federated learning (FL). This framework removes the central coordinating server used in conventional federated learning and instead requires participating nodes to ...
Ashley Allen +3 more
doaj +1 more source
Pathways and pitfalls: a qualitative study of student experiences in biomedical science education
Biomedical science students from underrepresented backgrounds face barriers including financial strain, disrupted laboratory access and cultural exclusion. Peer networks provide vital support when institutional systems are difficult to navigate. To create inclusive learning environments and achieve academic success, educators should blend active, hands‐
Olivia J. Russell +8 more
wiley +1 more source
This systematic review synthesizes prognostic models for survival and recurrence in resected non‐small cell lung cancer. While many models demonstrate moderate to good discrimination, few are externally validated and reporting quality is variable, limiting clinical applicability and highlighting the need for robust, transparent model development ...
Evangeline Samuel +4 more
wiley +1 more source
Personalised Peer-Supported Leraning: The Peer-to-Peer Learning environment (P2PLE)
The Peer-to-Peer Learning Environment (P2PLE) is a proposed approach to helpinglearners co-construct their learning environment using recommendations aboutpeople, content, and tools.
Joseph Corneli, Alexander Mikroyannidis
doaj
Intelligent Tutoring Systems for Adult Learning in STEM Disciplines
ABSTRACT Intelligent tutoring systems (ITS) are reshaping adult learning in STEM by providing adaptive, data‐driven instruction across classrooms, workplaces, and informal environments. In the context of ITS, this article compares generative AI, which creates personalized explanations and practice materials, with explainable AI, which focuses on ...
Jill Zarestky, Amanda R. Lager Gleason
wiley +1 more source

