#federated learning
37 papers match
TriShield: Zero-Utility-Loss Defense Against Privacy Backdoors in Federated Language Model Fine-Tuning via Orthogonal Gradient Projection and Optimizer State Entanglement
Cheng Wei
The paper introduces TriShield, a three‑component defense that eliminates privacy backdoor reconstruction attacks in federated fine‑tuning of large language models without any loss…
Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata
Michael Ben Ali, Imen Megdiche, André Péninou +1
The paper introduces FLAMECHE, a method that reformulates metadata‑based clustered federated learning as a distributed Expectation‑Maximization process, allowing additive server up…
Secure Aggregation for Privacy-Preserving Federated Learning on Clinical EEG Data
Pouya Rajabi, Mohsen Toorani
The paper proposes a federated learning framework for clinical EEG data that uses masking‑based secure aggregation and related cryptographic techniques to protect individual model…
Learning Color Grading, No Photo Sharing: Federated Aesthetic Preference Learning for Personalized Image Enhancement
Chuanzhi Xu, Ziyuan Tao, Jean Julien KNell +5
The paper proposes FedPAIE, a federated learning framework that learns individual aesthetic preferences for color grading and applies lightweight, personalized image enhancement on…
FedOGL: Combating Catastrophic Forgetting in Federated Open-World Multimodal Graph Learning
Zekai Chen, Haodong Lu, Shihao Li +5
The paper introduces FedOGL, a framework for federated multimodal graph learning that mitigates catastrophic forgetting by preserving semantic and structural memory through client-…
First-order Constrained Trilevel Optimization Over Distributed Networks for Robust Coreset Selection
Yang Jiao, Kaixuan Jiao, Kai Yang +4
The paper introduces F²CTO, a federated first-order constrained trilevel optimization algorithm for robust coreset selection across distributed edge networks, providing convergence…
Defending Against Backdoor Attacks via Alignment Checking in Model-Contrastive Federated Learning
Hongliang Zhang, Zhongyuan Yu, Guijuan Wang +4
The paper proposes FedDAB, a two‑phase defense for federated learning that uses contrastive regularization and alignment checking to detect and exclude malicious local updates caus…
An Informativeness-based Clustered Federated Learning Method for Reliable Traffic Prediction in Managed Wi-Fi Networks
Luca Barbieri, Gianluca Fontanesi, Lorenzo Galati Giordano +2
The paper proposes a clustered federated learning framework that selects informative AP clusters using differential entropy to improve Wi‑Fi traffic prediction while reducing commu…
Stable and Budget-Feasible Coalition Formation for Clustered Federated Learning: A Hedonic Potential-Game Approach
Cengis Hasan
The paper proposes a game-theoretic mechanism for forming stable, budget‑feasible coalitions in clustered federated learning, using a transferable‑surplus model and hedonic potenti…
FedWeave: Rethinking the Unit of Specialization in Heterogeneous Federated MoE-LoRA
Donghang Duan, Xu Zheng, Lizong Zhang +2
The paper introduces FedWeave, a federated learning framework that separates expert aggregation from router optimization to better handle heterogeneous tasks by using prototype-lev…
FedTopo: Relation-Level Topology Sharing for Model-Heterogeneous Federated Learning
Zhaoyang Ma, Zhihao Wu, Xin Gao +3
FedTopo introduces a method for federated learning with heterogeneous client models that shares class relation topologies instead of raw model parameters, enabling more reliable kn…
Collaborative System Failure Prognostics via Federated Longitudinal-Survival Modeling
Fan Yang, Madelyn Weller, Dimuthu Fernando +2
The paper proposes a federated learning framework that combines longitudinal sensor representation with a client‑separable discrete‑time hazard model to predict system failures and…
PIcsC: Partitioning-Induced Covariate Shift Correction
Behraj Khan, Behroz Mirza, Syed Ahmad Chan Bukhari +1
The paper introduces PIcsC, a Fisher information‑based regularization method that corrects covariate shift caused by data partitioning in both centralized (e.g., cross‑validation)…
Where Physics Meets Privacy: Federated PINNs for Privacy-Preserving Brain Tumor Biomechanical Modeling
Mahmuda Akter Sristy, Md Al-Mahfuz Chowdhury, Momota Ahsana Meem +2
The paper proposes a federated physics-informed neural network that learns patient-specific brain tumor biomechanical models from MRI data while keeping raw data on local sites, ac…
Studying Competing Events with Federated Cumulative Incidence Curves
Malcolm Risk, Shuang Yang, Jiang Bian +6
The paper introduces a federated learning approach for constructing non‑parametric cumulative incidence curves for competing risks without sharing patient‑level data, and applies i…
Knowledge-Aware Evolution for Task-Free Streaming Federated Continual Learning with Arbitrary Class Overlap
Sixing Tan, Xianmin Liu
The paper introduces FedKACE, a method for federated continual learning that works without task labels and handles streaming data with overlapping classes by adaptively switching i…
NFSA: Non-Forward Secure Aggregation with One Server via Two Layer Secret Sharing
Yufei Zhou
The paper introduces a secure aggregation protocol for federated learning that uses a two‑layer secret sharing scheme to eliminate server‑forwarded data, achieving one‑shot aggrega…
Energy-Efficient Federated Learning via Adaptive Encoder Freezing for MRI-to-CT Conversion: A Green AI-Guided Research
Ciro Benito Raggio, Lucia Migliorelli, Nils Skupien +6
The paper proposes an adaptive encoder‑freezing technique for federated learning that reduces energy use and CO2 emissions while preserving MRI‑to‑CT conversion quality.
EdgeFaaS: A Function-based Framework for Edge Computing
Neha Vadnere, Yu-Ting Wang, Yitao Chen +2
EdgeFaaS is a function‑as‑a‑service framework that abstracts heterogeneous IoT, edge, and cloud resources to run edge applications such as video analytics, federated learning, and…
What's in a Smoothness Constant? Tighter Rates for Local SGD with Bounded Second-order Heterogeneity
Kumar Kshitij Patel, Rustem Islamov, Sebastian U Stich +3
The paper establishes tighter convergence rates for Local SGD (Federated Averaging) on general convex problems under a bounded second‑order heterogeneity assumption, and provides n…
Mechanistic Evidence for Preserved-but-Misaligned Representations in Non-IID FedAvg
Muhammad Haseeb, Salaar Masood, Muhammad Abdullah Sohail +2
The paper investigates why federated averaging (FedAvg) performs poorly on non‑IID client data, finding that client models retain useful internal representations but these become m…
FM: Unified Federated Foundation Models for Heterogeneous Multimodal Medical Imaging
Shengchao Chen, Ting Shu
The paper introduces FM², a federated learning framework that trains a unified foundation model for heterogeneous multimodal medical images while preserving privacy, using dual Mix…
PriEval-Protect: A Unified Framework for Privacy Evaluation and Protection in Healthcare Systems
Ilef Chebil, Asma El Hadj, Souheib Yousfi +2
The paper introduces PriEval-Protect, a two‑phase framework that assesses privacy risks in healthcare systems by combining legal compliance scoring with technical data analysis, an…
Computation-aware Energy-harvesting Federated Learning with Pipelined Cyclic Scheduling
Eunjeong Jeong, Nikolaos Pappas
The paper introduces PipeCycle, a federated learning framework that groups clients into pipelined cyclic sets to overlap device recharging with training, reducing overall energy co…
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