Cross-Silo Federated Learning

Latest papers

13 papers2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Pricing Cross-Bank Context in Federated Anti-Money Laundering
Oct 26, 2026Lidia Losavio, Dario Fenoglio, Martin Gjoreski +1Cross-Silo Federated Learning Anti-Money Laundering Detection
Università della Svizzera italiana (USI), Lugano, Switzerland
FedRSPO+: A Heterogeneity-aware Algorithm for Decision-focused Federated Learning
Oct 6, 2026Konstantinos Ziliaskopoulos, Alexander Vinel, Jiaqi WangDecision-Focused Learning Federated Learning
Auburn University
When the Cross-Silo Federation Goes Offline: Continual Learning for Site Onboarding with Limited Unlabeled Data
Oct 4, 2026Ahmadreza Eslaminia, Klara Nahrstedt, Chenhui ShaoClass-Incremental Learning Federated Continual Learning
Coordinated Science Laboratory University of Illinois Urbana-Champaign · Dept. of Mechanical Engineering University of Michigan
HybridFLow: SDN-Orchestrated Client Partitioning for Hybrid Federated Learning
Sep 9, 2026Osama Abu Hamdan, Rabin Pandey, Hao Che +2Cross-Silo Federated Learning Asynchronous Federated Learning
University of Texas at Arlington · Missouri University of Science and Technology · Meta Platforms, Inc.
Federated Learning by Utility-Constrained Stochastic Aggregation for Improving Rational Participation
May 18, 2026M Yashwanth, Arunabh Singh, Ashok Nayak +2Federated Learning Aggregation Federated Learning
Indian Institute of Science, Bangalore · Indian Institute of Technology Bombay · IIIT Hyderabad
Cooperate to Compete: Strategic Data Generation and Incentivization Framework for Coopetitive Cross-Silo Federated Learning
Apr 16, 2026Thanh Linh Nguyen, Nguyen Van Huynh, Quoc-Viet PhamSynthetic Data Generation Cross-Silo Federated Learning
School of Computer Science and Statistics, Trinity College Dublin, Dublin 2, D02 PN40, Ireland · School of Computer Science and Informatics, University of Liverpool, Liverpool, L69 3DR, UK
Dynamic Free-Rider Detection in Cross-Silo Federated Learning via Simulated Attack Patterns
Apr 6, 2026Motoki NakamuraCross-Silo Federated Learning Federated Learning
Fujitsu Limited, Kanagawa, Japan
FedSQ: Optimized Weight Averaging via Fixed Gating
Apr 3, 2026Cristian Pérez-Corral, Jose I. Mestre, Alberto Fernández-Hernández +3Federated Learning Aggregation Federated Fine-Tuning
Universitat Politècnica de València Valencia, Spain · Universitat Jaume I Castelló de la Plana, Spain · Openchip & Software Technologies Barcelona, Spain
Revealing the influence of participant failures on model quality in cross-silo Federated Learning
Mar 26, 2026Fabian Stricker, David Bermbach, Christian ZirpinsCross-Silo Federated Learning Federated Learning
Scalable Cross-Facility Federated Learning for Scientific Foundation Models on Multiple Supercomputers
Mar 20, 2026Yijiang Li, Zilinghan Li, Kyle Chard +4Cross-Silo Federated Learning Federated Learning
Mathematics and Computer Science Division, Argonne National Laboratory · Data Science and Learning Division, Argonne National Laboratory · Department of Computer Science, University of Chicago
Resource-Adaptive Federated Text Generation with Differential Privacy
Mar 7, 2026Jiayi Wang, John Gounley, Heidi HansonDifferentially Private Federated Learning Synthetic Data Generation for Language Models
Oak Ridge National Laboratory Oak Ridge, TN, USA
Private and Robust Contribution Evaluation in Federated Learning
Feb 25, 2026Delio Jaramillo Velez, Gergely Biczok, Alexandre Graell i Amat +2Federated Learning Privacy-Preserving ML
University of La Laguna Tenerife, Spain · HUN-REN Hungarian Research Network Budapest, Hungary · Chalmers University Gotheburg, Sweden +2
Local Superior Soups: A Catalyst for Model Merging in Cross-Silo Federated Learning
Oct 31, 2024Minghui Chen, Meirui Jiang, Xin Zhang +3Communication-Efficient Distributed Training Cross-Silo Federated Learning
University of British Columbia · Vector Institute · Chinese University of Hong Kong +1

Oct 26, 2026J/K move · Enter open · S save

Pricing Cross-Bank Context in Federated Anti-Money Laundering

Lidia Losavio, Dario Fenoglio, Martin Gjoreski, Marc Langheinrich

Università della Svizzera italiana (USI), Lugano, Switzerland

Cross-Silo Federated Learning Anti-Money Laundering Detection Federated Graph Learning Federated Learning Differentially Private Federated Learning Graph Neural Networks

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Most graph-based Anti-Money Laundering (AML) methods assume access to a pooled transaction graph containing all accounts and payments, which in reality does not exist. Federated learning offers the necessary privacy guarantees but it remains unclear how money laundering patterns spanning separate graphs can effectively be detected. We exploit transactions already recorded by both endpoint banks to approximate remote account states locally, enabling multi-layer Graph Neural Network (GNN) computation without additional account-specific exchange. Starting from this baseline, we test whether the remaining context is worth sharing, using a disclosure ladder that adds two transaction counts per remote account or the counterparty's own GNN states. On AMLworld HI-Small, across six GNN backbones and up to 32 silos, the observable baseline yields the largest gain and keeps it as fragmentation shrinks the visible slice; counts add a robust correction, while exact remote states help only in specific regimes. In controlled privacy--utility studies, the noise needed to protect shared states erases their detection value before it buys meaningful privacy, whereas counts stay useful even under strong privacy protection. These findings support a practical rule for banks: use what they already record, and formally protect the little that must move.