Efficient and less centralized federated learning
With the rapid growth in mobile computing, massive amounts of data and computing
resources are now located at the edge. To this end, Federated learning (FL) is becoming a …
resources are now located at the edge. To this end, Federated learning (FL) is becoming a …
Fedmax: Mitigating activation divergence for accurate and communication-efficient federated learning
In this paper, we identify a new phenomenon called activation-divergence which occurs in
Federated Learning (FL) due to data heterogeneity (ie, data being non-IID) across multiple …
Federated Learning (FL) due to data heterogeneity (ie, data being non-IID) across multiple …
FAST: Enhancing Federated Learning Through Adaptive Data Sampling and Local Training
The emerging paradigm of federated learning (FL) strives to enable devices to cooperatively
train models without exposing their raw data. In most cases, the data across devices are non …
train models without exposing their raw data. In most cases, the data across devices are non …
High-efficient hierarchical federated learning on non-IID data with progressive collaboration
Hierarchical federated learning (HFL) allows multiple edge aggregations at edge devices
before one global aggregation to address both issues of non-independent and identically …
before one global aggregation to address both issues of non-independent and identically …
Hermes: an efficient federated learning framework for heterogeneous mobile clients
Federated learning (FL) has been a popular method to achieve distributed machine learning
among numerous devices without sharing their data to a cloud server. FL aims to learn a …
among numerous devices without sharing their data to a cloud server. FL aims to learn a …
Rethinking data heterogeneity in federated learning: Introducing a new notion and standard benchmarks
Though successful, federated learning presents new challenges for machine learning,
especially when the issue of data heterogeneity, also known as Non-IID data, arises. To …
especially when the issue of data heterogeneity, also known as Non-IID data, arises. To …
Fusion learning: A one shot federated learning
Federated Learning is an emerging distributed machine learning technique which does not
require the transmission of data to a central server to build a global model. Instead …
require the transmission of data to a central server to build a global model. Instead …
Federated Learning Can Find Friends That Are Beneficial
In Federated Learning (FL), the distributed nature and heterogeneity of client data present
both opportunities and challenges. While collaboration among clients can significantly …
both opportunities and challenges. While collaboration among clients can significantly …
Federated optimization in heterogeneous networks
Federated Learning is a distributed learning paradigm with two key challenges that
differentiate it from traditional distributed optimization:(1) significant variability in terms of the …
differentiate it from traditional distributed optimization:(1) significant variability in terms of the …
Federated learning algorithms with heterogeneous data distributions: An empirical evaluation
A Mora, D Fantini, P Bellavista - 2022 IEEE/ACM 7th …, 2022 - ieeexplore.ieee.org
Federated Learning (FL) is a paradigm that permits to learn a Deep Learning model without
centralizing raw data, and has recently received growing interest primarily as a solution to …
centralizing raw data, and has recently received growing interest primarily as a solution to …