Cover Image

WallStreetFeds: Client-Specific Tokens as Investment Vehicles in Federated Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Geimer, Arno, Pontiveros, Beltran Fiz, State, Radu
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

FedRandom: Sampling Consistent and Accurate Contribution Values in Federated Learning
by: Geimer, Arno, et al.
Published: (2026)

On the Volatility of Shapley-Based Contribution Metrics in Federated Learning
by: Geimer, Arno, et al.
Published: (2024)

Collaborative Batch Size Optimization for Federated Learning
by: Geimer, Arno, et al.
Published: (2025)

BouquetFL: Emulating diverse participant hardware in Federated Learning
by: Geimer, Arno
Published: (2026)

FedDAPL: Toward Client-Private Generalization in Federated Learning
by: Loaliyan, Soroosh Safari, et al.
Published: (2025)

Geometric Analysis of Token Selection in Multi-Head Attention
by: Mudarisov, Timur, et al.
Published: (2026)

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks
by: Wijaya, Tri Kurniawan, et al.
Published: (2025)

FedZMG: Efficient Client-Side Optimization in Federated Learning
by: Zantalis, Fotios, et al.
Published: (2026)

FedDPC : Handling Data Heterogeneity and Partial Client Participation in Federated Learning
by: Sen, Mrinmay, et al.
Published: (2025)

FedDAA: Dynamic Client Clustering for Concept Drift Adaptation in Federated Learning
by: Peng, Fu, et al.
Published: (2025)

FedConv: A Learning-on-Model Paradigm for Heterogeneous Federated Clients
by: Shen, Leming, et al.
Published: (2025)

FedRG: Unleashing the Representation Geometry for Federated Learning with Noisy Clients
by: Wen, Tian, et al.
Published: (2026)

FedCCRL: Federated Domain Generalization with Cross-Client Representation Learning
by: Wang, Xinpeng, et al.
Published: (2024)

FedOUI: OUI-Guided Client Weighting for Federated Aggregation
by: Fernández-Hernández, Alberto, et al.
Published: (2026)

FedAWA: Adaptive Optimization of Aggregation Weights in Federated Learning Using Client Vectors
by: Shi, Changlong, et al.
Published: (2025)

FedSat: A Statistical Aggregation Approach for Class Imbalanced Clients in Federated Learning
by: Chowdhury, Sujit, et al.
Published: (2024)

FedGT: Identification of Malicious Clients in Federated Learning with Secure Aggregation
by: Xhemrishi, Marvin, et al.
Published: (2023)

FedImpro: Measuring and Improving Client Update in Federated Learning
by: Tang, Zhenheng, et al.
Published: (2024)

FedAnchor: Enhancing Federated Semi-Supervised Learning with Label Contrastive Loss for Unlabeled Clients
by: Qiu, Xinchi, et al.
Published: (2024)

FedHybrid: Breaking the Memory Wall of Federated Learning via Hybrid Tensor Management
by: Tam, Kahou, et al.
Published: (2025)

FedSTaS: Client Stratification and Client Level Sampling for Efficient Federated Learning
by: Slessor, Jordan, et al.
Published: (2024)

FedSUM Family: Efficient Federated Learning Methods under Arbitrary Client Participation
by: You, Runze, et al.
Published: (2025)

FedDiverse: Tackling Data Heterogeneity in Federated Learning with Diversity-Driven Client Selection
by: Németh, Gergely D., et al.
Published: (2025)

Efficient Large-Scale Cross-Domain Sequential Recommendation with Dynamic State Representations
by: Loureiro, Manuel V., et al.
Published: (2025)

FedCAda: Adaptive Client-Side Optimization for Accelerated and Stable Federated Learning
by: Zhou, Liuzhi, et al.
Published: (2024)

FedHCA$^2$: Towards Hetero-Client Federated Multi-Task Learning
by: Lu, Yuxiang, et al.
Published: (2023)

GC-Fed: Gradient Centralized Federated Learning with Partial Client Participation
by: Seo, Jungwon, et al.
Published: (2025)

FedConPE: Efficient Federated Conversational Bandits with Heterogeneous Clients
by: Li, Zhuohua, et al.
Published: (2024)

FedABC: Attention-Based Client Selection for Federated Learning with Long-Term View
by: Ye, Wenxuan, et al.
Published: (2025)

FedHFT: Efficient Federated Finetuning with Heterogeneous Edge Clients
by: Ilhan, Fatih, et al.
Published: (2025)

FedCCA: Client-Centric Adaptation against Data Heterogeneity in Federated Learning on IoT Devices
by: Wang, Kaile, et al.
Published: (2026)

FedEve: On Bridging the Client Drift and Period Drift for Cross-device Federated Learning
by: Shen, Tao, et al.
Published: (2025)

FedAR: Addressing Client Unavailability in Federated Learning with Local Update Approximation and Rectification
by: Jiang, Chutian, et al.
Published: (2024)

FedOAED: Federated On-Device Autoencoder Denoiser for Heterogeneous Data under Limited Client Availability
by: Howlader, S M Ruhul Kabir, et al.
Published: (2025)

Variational Gaussian Mixture Manifold Models for Client-Specific Federated Personalization
by: Puppala, Sai, et al.
Published: (2025)

Geometric Entropy and Retrieval Phase Transitions in Continuous Thermal Dense Associative Memory
by: Petrova, Tatiana, et al.
Published: (2026)

FedReMa: Improving Personalized Federated Learning via Leveraging the Most Relevant Clients
by: Liang, Han, et al.
Published: (2024)

FedDiff: Diffusion Model Driven Federated Learning for Multi-Modal and Multi-Clients
by: Li, DaiXun, et al.
Published: (2023)

FedMMKT:Co-Enhancing a Server Text-to-Image Model and Client Task Models in Multi-Modal Federated Learning
by: He, Ningxin, et al.
Published: (2025)

Federated Learning with Workload Reduction through Partial Training of Client Models and Entropy-Based Data Selection
by: Shi, Hongrui, et al.
Published: (2024)