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Not All Federated Learning Algorithms Are Created Equal

Not All Federated Learning Algorithms Are Created Equal

Not All Federated Learning Algorithms Are Created Equal - arXiv

Title:Not All Federated Learning Algorithms Are Created Equal: A Performance Evaluation Study ... Abstract:Federated Learning (FL) emerged as a ...

Not All Federated Learning Algorithms Are Created Equal - arXiv

Our comprehensive measurement study reveals that no single algorithm works best across different performance metrics.

[PDF] Not All Federated Learning Algorithms Are Created Equal

It is revealed that no single algorithm works best across different performance metrics, and while some state-of-the-art algorithms achieve ...

Not All Federated Learning Algorithms Are Created Equal: A ...

... Learning. arXiv:2403.17287 (cs). [Submitted on 26 Mar 2024]. Title:Not All Federated Learning Algorithms Are Created Equal: A Performance Evaluation Study.

Revision History for Not All Federated Learning Algorithms...

Title: Not All Federated Learning Algorithms Are Created Equal: A Performance Evaluation Study · Authors: Gustav A. · Venue: CoRR 2024 · Venueid: dblp.org/journals ...

Evaluation and comparison of federated learning algorithms for ...

These experiments are all performed in the illustrative field of Human Activity Recognition (HAR) on smartphones. This domain aims to automatically identify ...

Can we use the word "federated learning" for non-machine learning ...

While the term "federated learning" is specifically associated with machine learning techniques and training models in a decentralized ...

Federated Learning - a tour of the problem, challenges ... - dataroots

The majority of machine learning algorithms are data hungry, the more the ... not necessarily the same but the samples come from the same subject. For ...

Federated Learning at the Network Edge: When Not All Nodes are ...

Request PDF | Federated Learning at the Network Edge: When Not All Nodes are Created Equal | Under the federated learning paradigm, a set of nodes can ...

A Survey of Trustworthy Federated Learning: Issues, Solutions, and ...

[175] introduce a federated boosting algorithm based on distributed AdaBoost, specifically designed ... Not All Samples Are Created Equal: Deep Learning with ...

FedPerf: A Practitioners' Guide to Performance of Federated ...

In practice, exploring the right set of configuration settings for an FL algorithm is a costly and arduous task. The primary reason being that training FL ...

Reviewing Federated Learning Aggregation Algorithms - MDPI

The core idea of this technique is that, unlike traditional machine learning, user data is not collected on a central server. Nevertheless, models are sent to ...

[PDF] Federated Learning at the Network Edge: When Not All Nodes ...

... learning model with the ... Federated Learning at the Network Edge: When Not All Nodes Are Created Equal ... algorithms, which consider a large set of ...

Federated learning with hyper-parameter optimization - ScienceDirect

Federated Learning is a new approach for distributed training of a deep learning model on data scattered across a large number of clients while ensuring ...

Federated Learning Algorithms to Optimize the Client and Cost ...

If all clients are allowed to participate in the training process of federated learning, there will be participants with backward iterations. If ...

[D] Why are most Federated Learning methods so dependent on ...

I don't know about federated learning specifically but all deep learning methods are dependent on hyperparameters to a certain degree. Some more ...

Federated Learning in AI: How It Works, Benefits and Challenges

... algorithms do not allow such provisions or if the necessary ... create a reflection of the combined learning of all participating devices.

Theory and Algorithms for Communication- and Computation ...

To tackle this challenge, federated learning (FL) has been proposed as an alternative learning paradigm, the key idea of which is to move data ...

Importance of Federated Learning Collaboratives | Medical School

Federated learning encourages the creation of interoperable AI tools that can be applied across different systems, which is critical for scaling innovations in ...

Targeting Fair Competition in Personalized Federated Learning

Lower bounds and optimal algorithms for personalized feder- ated learning. ... Not all samples are created equal: Deep learning with importance sampling.