Personalized Federated Learning through Local Memorization
- URL: http://arxiv.org/abs/2111.09360v1
- Date: Wed, 17 Nov 2021 19:40:07 GMT
- Title: Personalized Federated Learning through Local Memorization
- Authors: Othmane Marfoq, Giovanni Neglia, Laetitia Kameni, Richard Vidal
- Abstract summary: Federated learning allows clients to collaboratively learn statistical models while keeping their data local.
Recent personalized federated learning methods train a separate model for each client while still leveraging the knowledge available at other clients.
We show on a suite of federated datasets that this approach achieves significantly higher accuracy and fairness than state-of-the-art methods.
- Score: 10.925242558525683
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Federated learning allows clients to collaboratively learn statistical models
while keeping their data local. Federated learning was originally used to train
a unique global model to be served to all clients, but this approach might be
sub-optimal when clients' local data distributions are heterogeneous. In order
to tackle this limitation, recent personalized federated learning methods train
a separate model for each client while still leveraging the knowledge available
at other clients. In this work, we exploit the ability of deep neural networks
to extract high quality vectorial representations (embeddings) from non-tabular
data, e.g., images and text, to propose a personalization mechanism based on
local memorization. Personalization is obtained interpolating a pre-trained
global model with a $k$-nearest neighbors (kNN) model based on the shared
representation provided by the global model. We provide generalization bounds
for the proposed approach and we show on a suite of federated datasets that
this approach achieves significantly higher accuracy and fairness than
state-of-the-art methods.
Related papers
- Personalized Federated Learning via Feature Distribution Adaptation [3.410799378893257]
Federated learning (FL) is a distributed learning framework that leverages commonalities between distributed client datasets to train a global model.
personalized federated learning (PFL) seeks to address this by learning individual models tailored to each client.
We propose an algorithm, pFedFDA, that efficiently generates personalized models by adapting global generative classifiers to their local feature distributions.
arXiv Detail & Related papers (2024-11-01T03:03:52Z) - Proximity-based Self-Federated Learning [1.0066310107046081]
This paper introduces a novel, fully-distributed federated learning strategy called proximity-based self-federated learning.
Unlike traditional algorithms, our approach encourages clients to share and adjust their models with neighbouring nodes based on geographic proximity and model accuracy.
arXiv Detail & Related papers (2024-07-17T08:44:45Z) - Federated Learning with Projected Trajectory Regularization [65.6266768678291]
Federated learning enables joint training of machine learning models from distributed clients without sharing their local data.
One key challenge in federated learning is to handle non-identically distributed data across the clients.
We propose a novel federated learning framework with projected trajectory regularization (FedPTR) for tackling the data issue.
arXiv Detail & Related papers (2023-12-22T02:12:08Z) - Dirichlet-based Uncertainty Quantification for Personalized Federated
Learning with Improved Posterior Networks [9.54563359677778]
This paper presents a new approach to federated learning that allows selecting a model from global and personalized ones.
It is achieved through a careful modeling of predictive uncertainties that helps to detect local and global in- and out-of-distribution data.
The comprehensive experimental evaluation on the popular real-world image datasets shows the superior performance of the model in the presence of out-of-distribution data.
arXiv Detail & Related papers (2023-12-18T14:30:05Z) - Visual Prompt Based Personalized Federated Learning [83.04104655903846]
We propose a novel PFL framework for image classification tasks, dubbed pFedPT, that leverages personalized visual prompts to implicitly represent local data distribution information of clients.
Experiments on the CIFAR10 and CIFAR100 datasets show that pFedPT outperforms several state-of-the-art (SOTA) PFL algorithms by a large margin in various settings.
arXiv Detail & Related papers (2023-03-15T15:02:15Z) - Decentralised Person Re-Identification with Selective Knowledge
Aggregation [56.40855978874077]
Existing person re-identification (Re-ID) methods mostly follow a centralised learning paradigm which shares all training data to a collection for model learning.
Two recent works have introduced decentralised (federated) Re-ID learning for constructing a globally generalised model (server)
However, these methods are poor on how to adapt the generalised model to maximise its performance on individual client domain Re-ID tasks.
We present a new Selective Knowledge Aggregation approach to decentralised person Re-ID to optimise the trade-off between model personalisation and generalisation.
arXiv Detail & Related papers (2021-10-21T18:09:53Z) - Federated Multi-Task Learning under a Mixture of Distributions [10.00087964926414]
Federated Learning (FL) is a framework for on-device collaborative training of machine learning models.
First efforts in FL focused on learning a single global model with good average performance across clients, but the global model may be arbitrarily bad for a given client.
We study federated MTL under the flexible assumption that each local data distribution is a mixture of unknown underlying distributions.
arXiv Detail & Related papers (2021-08-23T15:47:53Z) - Towards Fair Federated Learning with Zero-Shot Data Augmentation [123.37082242750866]
Federated learning has emerged as an important distributed learning paradigm, where a server aggregates a global model from many client-trained models while having no access to the client data.
We propose a novel federated learning system that employs zero-shot data augmentation on under-represented data to mitigate statistical heterogeneity and encourage more uniform accuracy performance across clients in federated networks.
We study two variants of this scheme, Fed-ZDAC (federated learning with zero-shot data augmentation at the clients) and Fed-ZDAS (federated learning with zero-shot data augmentation at the server).
arXiv Detail & Related papers (2021-04-27T18:23:54Z) - Exploiting Shared Representations for Personalized Federated Learning [54.65133770989836]
We propose a novel federated learning framework and algorithm for learning a shared data representation across clients and unique local heads for each client.
Our algorithm harnesses the distributed computational power across clients to perform many local-updates with respect to the low-dimensional local parameters for every update of the representation.
This result is of interest beyond federated learning to a broad class of problems in which we aim to learn a shared low-dimensional representation among data distributions.
arXiv Detail & Related papers (2021-02-14T05:36:25Z) - Personalized Federated Learning with First Order Model Optimization [76.81546598985159]
We propose an alternative to federated learning, where each client federates with other relevant clients to obtain a stronger model per client-specific objectives.
We do not assume knowledge of underlying data distributions or client similarities, and allow each client to optimize for arbitrary target distributions of interest.
Our method outperforms existing alternatives, while also enabling new features for personalized FL such as transfer outside of local data distributions.
arXiv Detail & Related papers (2020-12-15T19:30:29Z) - Specialized federated learning using a mixture of experts [0.6974741712647655]
In federated learning, clients share a global model that has been trained on decentralized local client data.
We propose an alternative method to learn a personalized model for each client in a federated setting.
Our results show that the mixture of experts model is better suited as a personalized model for devices in these settings.
arXiv Detail & Related papers (2020-10-05T14:43:57Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.