SalientGrads: Sparse Models for Communication Efficient and Data Aware
Distributed Federated Training
- URL: http://arxiv.org/abs/2304.07488v1
- Date: Sat, 15 Apr 2023 06:46:37 GMT
- Title: SalientGrads: Sparse Models for Communication Efficient and Data Aware
Distributed Federated Training
- Authors: Riyasat Ohib, Bishal Thapaliya, Pratyush Gaggenapalli, Jingyu Liu,
Vince Calhoun, Sergey Plis
- Abstract summary: Federated learning (FL) enables the training of a model leveraging decentralized data in client sites while preserving privacy by not collecting data.
One of the significant challenges of FL is limited computation and low communication bandwidth in resource limited edge client nodes.
We propose Salient Grads, which simplifies the process of sparse training by choosing a data aware subnetwork before training.
- Score: 1.0413504599164103
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Federated learning (FL) enables the training of a model leveraging
decentralized data in client sites while preserving privacy by not collecting
data. However, one of the significant challenges of FL is limited computation
and low communication bandwidth in resource limited edge client nodes. To
address this, several solutions have been proposed in recent times including
transmitting sparse models and learning dynamic masks iteratively, among
others. However, many of these methods rely on transmitting the model weights
throughout the entire training process as they are based on ad-hoc or random
pruning criteria. In this work, we propose Salient Grads, which simplifies the
process of sparse training by choosing a data aware subnetwork before training,
based on the model-parameter's saliency scores, which is calculated from the
local client data. Moreover only highly sparse gradients are transmitted
between the server and client models during the training process unlike most
methods that rely on sharing the entire dense model in each round. We also
demonstrate the efficacy of our method in a real world federated learning
application and report improvement in wall-clock communication time.
Related papers
- Cohort Squeeze: Beyond a Single Communication Round per Cohort in Cross-Device Federated Learning [51.560590617691005]
We investigate whether it is possible to squeeze more juice" out of each cohort than what is possible in a single communication round.
Our approach leads to up to 74% reduction in the total communication cost needed to train a FL model in the cross-device setting.
arXiv Detail & Related papers (2024-06-03T08:48:49Z) - HierSFL: Local Differential Privacy-aided Split Federated Learning in
Mobile Edge Computing [7.180235086275924]
Federated Learning is a promising approach for learning from user data while preserving data privacy.
Split Federated Learning is utilized, where clients upload their intermediate model training outcomes to a cloud server for collaborative server-client model training.
This methodology facilitates resource-constrained clients' participation in model training but also increases the training time and communication overhead.
We propose a novel algorithm, called Hierarchical Split Federated Learning (HierSFL), that amalgamates models at the edge and cloud phases.
arXiv Detail & Related papers (2024-01-16T09:34:10Z) - Tunable Soft Prompts are Messengers in Federated Learning [55.924749085481544]
Federated learning (FL) enables multiple participants to collaboratively train machine learning models using decentralized data sources.
The lack of model privacy protection in FL becomes an unneglectable challenge.
We propose a novel FL training approach that accomplishes information exchange among participants via tunable soft prompts.
arXiv Detail & Related papers (2023-11-12T11:01:10Z) - FedLALR: Client-Specific Adaptive Learning Rates Achieve Linear Speedup
for Non-IID Data [54.81695390763957]
Federated learning is an emerging distributed machine learning method.
We propose a heterogeneous local variant of AMSGrad, named FedLALR, in which each client adjusts its learning rate.
We show that our client-specified auto-tuned learning rate scheduling can converge and achieve linear speedup with respect to the number of clients.
arXiv Detail & Related papers (2023-09-18T12:35:05Z) - Federated Learning on Non-iid Data via Local and Global Distillation [25.397058380098816]
We propose FedND: federated learning with noise distillation.
In the client, we propose a self-distillation method to train the local model.
In the server, we generate noisy samples for each client and use them to distill other clients.
Experimental results show that the algorithm achieves the best performance and is more communication-efficient than state-of-the-art methods.
arXiv Detail & Related papers (2023-06-26T06:14:01Z) - DisPFL: Towards Communication-Efficient Personalized Federated Learning
via Decentralized Sparse Training [84.81043932706375]
We propose a novel personalized federated learning framework in a decentralized (peer-to-peer) communication protocol named Dis-PFL.
Dis-PFL employs personalized sparse masks to customize sparse local models on the edge.
We demonstrate that our method can easily adapt to heterogeneous local clients with varying computation complexities.
arXiv Detail & Related papers (2022-06-01T02:20:57Z) - One-shot Federated Learning without Server-side Training [42.59845771101823]
One-shot federated learning is gaining popularity as a way to reduce communication cost between clients and the server.
Most of the existing one-shot FL methods are based on Knowledge Distillation; however, distillation based approach requires an extra training phase and depends on publicly available data sets or generated pseudo samples.
In this work, we consider a novel and challenging cross-silo setting: performing a single round of parameter aggregation on the local models without server-side training.
arXiv Detail & Related papers (2022-04-26T01:45:37Z) - Acceleration of Federated Learning with Alleviated Forgetting in Local
Training [61.231021417674235]
Federated learning (FL) enables distributed optimization of machine learning models while protecting privacy.
We propose FedReg, an algorithm to accelerate FL with alleviated knowledge forgetting in the local training stage.
Our experiments demonstrate that FedReg not only significantly improves the convergence rate of FL, especially when the neural network architecture is deep.
arXiv Detail & Related papers (2022-03-05T02:31:32Z) - A Personalized Federated Learning Algorithm: an Application in Anomaly
Detection [0.6700873164609007]
Federated Learning (FL) has recently emerged as a promising method to overcome data privacy and transmission issues.
In FL, datasets collected from different devices or sensors are used to train local models (clients) each of which shares its learning with a centralized model (server)
This paper proposes a novel Personalized FedAvg (PC-FedAvg) which aims to control weights communication and aggregation augmented with a tailored learning algorithm to personalize the resulting models at each client.
arXiv Detail & Related papers (2021-11-04T04:57:11Z) - FedKD: Communication Efficient Federated Learning via Knowledge
Distillation [56.886414139084216]
Federated learning is widely used to learn intelligent models from decentralized data.
In federated learning, clients need to communicate their local model updates in each iteration of model learning.
We propose a communication efficient federated learning method based on knowledge distillation.
arXiv Detail & Related papers (2021-08-30T15:39:54Z) - On the Convergence Time of Federated Learning Over Wireless Networks
Under Imperfect CSI [28.782485580296374]
We propose a training process that takes channel statistics as a bias to minimize the convergence time under imperfect CSI.
We also examine the trade-off between number of clients involved in the training process and model accuracy as a function of different fading regimes.
arXiv Detail & Related papers (2021-04-01T08:30:45Z)
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.