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Found 13 papers, 4 papers with code
Cross-Modal Prototype based Multimodal Federated Learning under Severely Missing Modality
In this paper, we propose Multimodal Federated Cross Prototype Learning (MFCPL), a novel approach for MFL under severely missing modalities by conducting the complete prototypes to provide diverse modality knowledge in modality-shared level with the cross-modal regularization and modality-specific level with cross-modal contrastive mechanism.
LW-FedSSL: Resource-efficient Layer-wise Federated Self-supervised Learning
Many recent studies integrate federated learning (FL) with self-supervised learning (SSL) to take advantage of raw training data distributed across edge devices.
OnDev-LCT: On-Device Lightweight Convolutional Transformers towards federated learning
Federated learning (FL) has emerged as a promising approach to collaboratively train machine learning models across multiple edge devices while preserving privacy.
Contrastive encoder pre-training-based clustered federated learning for heterogeneous data
Together, self-supervised pre-training and client clustering can be crucial components for tackling the data heterogeneity issues of FL.
FedMEKT: Distillation-based Embedding Knowledge Transfer for Multimodal Federated Learning
Bringing this concept into a system, we develop a distillation-based multimodal embedding knowledge transfer mechanism, namely FedMEKT, which allows the server and clients to exchange the joint knowledge of their learning models extracted from a small multimodal proxy dataset.
CDKT-FL: Cross-Device Knowledge Transfer using Proxy Dataset in Federated Learning
Therefore, to develop robust generalized global and personalized models, conventional FL methods need redesigning the knowledge aggregation from biased local models while considering huge divergence of learning parameters due to skewed client data.
Edge-assisted Democratized Learning Towards Federated Analytics
Therefore, we need to design a robust learning mechanism than the FL that (i) unleashes a viable infrastructure for FA and (ii) trains learning models with better generalization capability.
Toward Multiple Federated Learning Services Resource Sharing in Mobile Edge Networks
Federated Learning is a new learning scheme for collaborative training a shared prediction model while keeping data locally on participating devices.
An Incentive Mechanism for Federated Learning in Wireless Cellular network: An Auction Approach
In this paper, we consider a FL system that involves one base station (BS) and multiple mobile users.
Self-organizing Democratized Learning: Towards Large-scale Distributed Learning Systems
Inspired by Dem-AI philosophy, a novel distributed learning approach is proposed in this paper.
Distributed and Democratized Learning: Philosophy and Research Challenges
Consequently, many emerging cross-device AI applications will require a transition from traditional centralized learning systems towards large-scale distributed AI systems that can collaboratively perform multiple complex learning tasks.
Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism
IoT devices with intelligence require the use of effective machine learning paradigms.
Distributed, Parallel, and Cluster Computing
Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocation
There is an increasing interest in a fast-growing machine learning technique called Federated Learning, in which the model training is distributed over mobile user equipments (UEs), exploiting UEs' local computation and training data.
