Latest Federated Learning Research Papers

The newest Federated Learning papers from across the field — arXiv, NeurIPS, CVPR, Nature, and more — refreshed daily and ranked by relevance. Distill AI tracks Federated Learning so you don’t have to: get the standout work delivered to your inbox every morning, with 2-sentence summaries and the option to chat with any paper.

Recent papers

• OncoTraj: a public benchmark for longitudinal resistance prediction in EGFR-mutant non-small-cell lung cancer on osimertinib
  Abhijoy Sarkar, Aarchi Singh Thakur · arXiv · Jun 9, 2026

  Resistance to first-line osimertinib in EGFR-mutant non-small-cell lung cancer (NSCLC) is the canonical example of predictable clonal evolution under therapeutic pressure, yet no public benchmark exists for training or evaluating computatio…

• IntraShuffler: A Privacy Preserving Framework for Heterogeneous DP Federated Learning
  Farhin Farhad Riya, Olivera Kotevska, Jinyuan Stella Sun · arXiv · Jun 1, 2026

  Heterogeneous Differential Privacy (HDP) in Federated Learning (FL) allows clients to select individual privacy budgets ($\varepsilon_i$) according to institutional policies and data sensitivity. In practice, many HDP-FL systems employ $\va…

• Fairness-Aware Federated Learning with Trajectory Shapley Value
  Daniel Kuznetsov, Ziqi Wang · arXiv · May 28, 2026

  Federated learning is an emerging distributed paradigm that addresses the challenges posed by heterogeneous, privacy-sensitive data. It enables multiple clients to train a model collaboratively by aggregating their local updates at a server…

• Federated Reinforcement Learning for Efficient Mobile Crowdsensing under Incomplete Information
  Sumedh J. Dongare, Patrick Weber, Andrea Ortiz, Walid Saad et al. · arXiv · May 4, 2026

  Mobile crowdsensing (MCS) is a distributed sensing architecture that utilizes existing sensors on mobile units (MUs) to perform sensing tasks. A mobile crowdsensing platform (MCSP) publishes the sensing tasks and the MUs decide whether to p…

• EASE: Federated Multimodal Unlearning via Entanglement-Aware Anchor Closure
  Zihao Ding, Beining Wu, Jun Huang · arXiv · May 1, 2026

  Federated Multimodal Learning (FML) trains multimodal models across decentralized clients while keeping their image-text pairs private. However, joint embedding training entangles forgotten knowledge across both modalities and client gradie…

• FedKPer: Tackling Generalization and Personalization in Medical Federated Learning via Knowledge Personalization
  Zoe Fowler, Ghassan AlRegib · arXiv · May 1, 2026

  Federated learning (FL) holds great potential for medical applications. However, statistical heterogeneity across healthcare institutions poses a major challenge for FL, as the global model struggles both to generalize across unseen patient…

• Turning the TIDE: Cross-Architecture Distillation for Diffusion Large Language Models
  Gongbo Zhang, Wen Wang, Ye Tian, Li Yuan · arXiv · Apr 29, 2026

  Diffusion large language models (dLLMs) offer parallel decoding and bidirectional context, but state-of-the-art dLLMs require billions of parameters for competitive performance. While existing distillation methods for dLLMs reduce inference…

• Asynchronous Federated Unlearning with Invariance Calibration for Medical Imaging
  Zhaoyuan Cai, Xinglin Zhang · arXiv · Apr 29, 2026

  Federated Unlearning (FU) is an emerging paradigm in Federated Learning (FL) that enables participating clients to fully remove their contributions from a trained global model, driven by data protection regulations that mandate the right to…

• FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels
  Sina Gholami, Abdulmoneam Ali, Tania Haghighi, Ahmed Arafa et al. · arXiv · Apr 22, 2026

  Federated learning (FL) enables collaborative model training without sharing raw data; however, the presence of noisy labels across distributed clients can severely degrade the learning performance. In this paper, we propose FedSIR, a multi…

• Lifecycle-Aware Federated Continual Learning in Mobile Autonomous Systems
  Beining Wu, Jun Huang · arXiv · Apr 22, 2026

  Federated continual learning (FCL) allows distributed autonomous fleets to adapt collaboratively to evolving terrain types across extended mission lifecycles. However, current approaches face several key challenges: 1) they use uniform prot…

• FB-NLL: A Feature-Based Approach to Tackle Noisy Labels in Personalized Federated Learning
  Abdulmoneam Ali, Ahmed Arafa · arXiv · Apr 21, 2026

  Personalized Federated Learning (PFL) aims to learn multiple task-specific models rather than a single global model across heterogeneous data distributions. Existing PFL approaches typically rely on iterative optimization-such as model upda…

• VLA Foundry: A Unified Framework for Training Vision-Language-Action Models
  Jean Mercat, Sedrick Keh, Kushal Arora, Isabella Huang et al. · arXiv · Apr 21, 2026

  We present VLA Foundry, an open-source framework that unifies LLM, VLM, and VLA training in a single codebase. Most open-source VLA efforts specialize on the action training stage, often stitching together incompatible pretraining pipelines…

• Synthetic Data Meets Finance: Generative Models for Privacy Preserving Analytics
  Yongbin Yang, Jingyun Yang · Journal of Banking and Fina... · Apr 21, 2026

  The financial industry faces increasing pressure from privacy regulations, including the General Data Protection Regulation (GDPR) and sector-specific compliance frameworks, which restrict access to sensitive transaction data critical for t…

• EPRA-VFL: A privacy-preserving and efficient verifiable federated learning scheme with robust aggregation
  Yujie Li, Yi Sun, Lu Guo, Zhijie Guo et al. · Journal of Parallel and Dis... · Mar 24, 2026
• <b>THE FEDERATIVE PACT AND MUNICIPAL PUBLIC POLICIES: COOPERATION, DECENTRALIZATION, AND EDUCATION FINANCING</b>
  Antonio Roberto Xavier, GILSON ADÃO DOMINGOS VIEIRA, Fidel Cambundo Sanuca, Edmilson Alberto Matamba et al. · Journal International Revie... · Mar 24, 2026

  The main objective of this work is to investigate the level of cooperation, decentralization, and dialogue that exists between municipalities due to the federal pact. The 1988 Federal Constitution established the federal pact with a peculia…

• FedLTN-CubeSat: Neuro-Symbolic Federated Learning for Intrusion Detection in LEO CubeSat Constellations
  Gang Yang, Lin Ni, Junfeng Geng, Xiang Peng · Mathematics · Mar 20, 2026

  Low Earth Orbit (LEO) mega-constellations are becoming the backbone of global communications, yet their cybersecurity remains critically under-addressed. Intrusion detection systems (IDSs) for such constellations face a unique trilemma of a…

• Zero-Knowledge Federated Learning: A New Trustworthy and Privacy-Preserving Distributed Learning Paradigm
  Taotao Wang, Yuxin Jin, Qing Yang, Yihan Xia et al. · IEEE Communications Magazine · Mar 18, 2026

  Federated Learning (FL) has emerged as a promising paradigm in distributed machine learning, enabling collaborative model training while preserving data privacy. FL still contends with significant challenges- most notably regarding security…

• Ontology- and LLM-based data harmonization for federated learning in healthcare
  Natallia Kokash, Lei Wang, Tom Gillespie, Adam Belloum et al. · Frontiers in Digital Health · Mar 18, 2026

  Introduction: Semantic heterogeneity across electronic health records (EHRs) limits scalable and privacy-preserving analytics in healthcare. While federated learning (FL) enables collaborative modeling without sharing raw data, it requires …

• Federation Opacity and the Promise of Federated Learning in Healthcare
  Joshua Hatherley, Anders Søgaard, Angela Ballantyne, Ruben Pauwels · The American Journal of Bio... · Mar 5, 2026

  fairness and accountability in medical ML; and (b) makes FL models especially vulnerable to data poisoning attacks. It then identifies several key claims about the expected benefits of FL in healthcare and argues that they may be either exa…

• TrustGraph: Federated Graph Neural Networks for Cross-Platform Trust and Fraud Propagation Analysis
  Tejas Patel, Arun Kumar, Madhushree Kumari, Rajesh Purushothaman et al. · OpenAlex · Feb 18, 2026

  Centralized fraud detection systems in e-commerce ecosystems face significant limitations due to stringent data privacy regulations, platform heterogeneity, and the distributed nature of sophisticated fraud rings operating across multiple m…

• FraudSentinel: Federated Multi-Agent Reinforcement Learning for Privacy-Preserving Cross-Marketplace Fraud Detection in Distributed E-Commerce Ecosystems
  Tejas Patel, Chaitanya Kulkarni, Deepak Kole, Milan Parikh et al. · OpenAlex · Feb 18, 2026

  E-commerce fraud costs the global economy $48 billion annually, with sophisticated fraud rings operating across multiple online marketplaces. While individual platforms deploy fraud detection systems, fraudsters exploit the lack of cross-pl…

• Federated Learning-Based Intrusion Detection in IoT Networks: Performance Evaluation and Data Scaling Study
  Nurtay Albanbay, Yerlan Tursynbek, Kalman Graffi, R. Uskenbayeva et al. · J. Sens. Actuator Networks · Jul 23, 2025

  This paper presents a large-scale empirical study aimed at identifying the optimal local deep learning model and data volume for deploying intrusion detection systems (IDS) on resource-constrained IoT devices using federated learning (FL). …

• Revolutionizing healthcare data analytics with federated learning: A comprehensive survey of applications, systems, and future directions
  N. Madathil, F. Dankar, Marton Gergely, Abdelkader Nasreddine Belkacem et al. · Computational and Structural Biotechnology Journal · Jun 1, 2025

  Federated learning (FL)–a distributed machine learning that offers collaborative training of global models across multiple clients. FL has been considered for the design and development of many FL systems in various domains. Hence, we prese…

• Intrusion Detection Based on Federated Learning: A Systematic Review
  J. L. Hernández-Ramos, Georgios Karopoulos, Efstratios Chatzoglou, Vasileios Kouliaridis et al. · ACM Computing Surveys · Apr 23, 2025

  The evolution of cybersecurity is closely linked to the development and improvement of artificial intelligence (AI). As a key tool for realizing more cybersecure ecosystems, Intrusion Detection Systems (IDSs) have evolved tremendously in re…

• Federated learning with differential privacy for breast cancer diagnosis enabling secure data sharing and model integrity
  Shubhi Shukla, S. Rajkumar, Aditisinha Sinha, Mohamed Esha et al. · Scientific Reports · Apr 16, 2025

  In the digital age, privacy preservation is of paramount importance while processing health-related sensitive information. This paper explores the integration of Federated Learning (FL) and Differential Privacy (DP) for breast cancer detect…

• Privacy-preserving federated learning for collaborative medical data mining in multi-institutional settings
  Rahul Haripriya, Nilay Khare, Manish Pandey · Scientific Reports · Apr 11, 2025

  Ensuring data privacy in medical image classification is a critical challenge in healthcare, especially with the increasing reliance on AI-driven diagnostics. In fact, over 30% of healthcare organizations globally have experienced a data br…

• Federated learning with LSTM for intrusion detection in IoT-based wireless sensor networks: a multi-dataset analysis
  R. W. Anwar, Mohammad Abrar, Abdu Salam, Faizan Ullah · PeerJ Computer Science · Mar 28, 2025

  Intrusion detection in Internet of Things (IoT)-based wireless sensor networks (WSNs) is essential due to their widespread use and inherent vulnerability to security breaches. Traditional centralized intrusion detection systems (IDS) face s…

• Secure and Transparent Banking: Explainable AI-Driven Federated Learning Model for Financial Fraud Detection
  Saif Khalifa Aljunaid, Saif Almheiri, Hussain Dawood, Muhammad Adnan Khan · Journal of Risk and Financial Management · Mar 27, 2025

  The increasing sophistication of fraud has rendered rule-based fraud detection obsolete, exposing banks to greater financial risk, reputational damage, and regulatory penalties. Financial stability, customer trust, and compliance are increa…

• An optimal federated learning-based intrusion detection for IoT environment
  A. Karunamurthy, K. Vijayan, Pravin R. Kshirsagar, Kuan Tak Tan · Scientific Reports · Mar 13, 2025

  Federated Learning (FL) allows the learning models in distributed systems to be trained by sharing the network data and model parameters. The attack patterns of attackers are frequently upgraded as well as the technology improves. Machine l…

• Federated Learning for Cloud and Edge Security: A Systematic Review of Challenges and AI Opportunities
  Latifa Albshaier, Seetah Almarri, A. Albuali · Electronics · Mar 3, 2025

  The ongoing evolution of cloud computing requires sustained attention to security, privacy, and compliance issues. The purpose of this paper is to systematically review the current literature regarding the application of federated learning …