Prune at the Clients, Not the Server: Accelerated Sparse Training in Federated Learning

• URL: http://arxiv.org/abs/2405.20623v1
• Date: Fri, 31 May 2024 05:21:12 GMT
• Title: Prune at the Clients, Not the Server: Accelerated Sparse Training in Federated Learning
• Authors: Georg Meinhardt, Kai Yi, Laurent Condat, Peter Richtárik,
• Abstract summary: Resource constraints of clients and communication costs pose major problems for training large models in Federated Learning. We introduce Sparse-ProxSkip, which combines training and acceleration in a sparse setting. We demonstrate the good performance of Sparse-ProxSkip in extensive experiments.
• Score: 56.21666819468249
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the recent paradigm of Federated Learning (FL), multiple clients train a shared model while keeping their local data private. Resource constraints of clients and communication costs pose major problems for training large models in FL. On the one hand, addressing the resource limitations of the clients, sparse training has proven to be a powerful tool in the centralized setting. On the other hand, communication costs in FL can be addressed by local training, where each client takes multiple gradient steps on its local data. Recent work has shown that local training can provably achieve the optimal accelerated communication complexity [Mishchenko et al., 2022]. Hence, one would like an accelerated sparse training algorithm. In this work we show that naive integration of sparse training and acceleration at the server fails, and how to fix it by letting the clients perform these tasks appropriately. We introduce Sparse-ProxSkip, our method developed for the nonconvex setting, inspired by RandProx [Condat and Richt\'arik, 2022], which provably combines sparse training and acceleration in the convex setting. We demonstrate the good performance of Sparse-ProxSkip in extensive experiments.

Related papers

• FedComLoc: Communication-Efficient Distributed Training of Sparse and Quantized Models [56.21666819468249]
  Federated Learning (FL) has garnered increasing attention due to its unique characteristic of allowing heterogeneous clients to process their private data locally and interact with a central server. We introduce FedComLoc, integrating practical and effective compression into emphScaffnew to further enhance communication efficiency.
  arXiv  Detail & Related papers  (2024-03-14T22:29:59Z)
• 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)
• 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)
• SalientGrads: Sparse Models for Communication Efficient and Data Aware Distributed Federated Training [1.0413504599164103]
  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.
  arXiv  Detail & Related papers  (2023-04-15T06:46:37Z)
• Accelerating Hybrid Federated Learning Convergence under Partial Participation [14.427308569399957]
  Federated Learning (FL) involves a group of clients with decentralized data who collaborate to learn a common model. In realistic scenarios, the server may be able to collect a small amount of data that approximately mimics the population distribution. We propose a new algorithm called FedCLG, which investigates the two-fold role of the server in hybrid FL.
  arXiv  Detail & Related papers  (2023-04-10T19:13:14Z)
• Aergia: Leveraging Heterogeneity in Federated Learning Systems [5.0650178943079]
  Federated Learning (FL) relies on clients to update a global model using their local datasets. Aergia is a novel approach where slow clients freeze the part of their model that is the most computationally intensive to train. Aergia significantly reduces the training time under heterogeneous settings by up to 27% and 53% compared to FedAvg and TiFL, respectively.
  arXiv  Detail & Related papers  (2022-10-12T12:59:18Z)
• 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)
• 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)
• Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocation [119.19061102064497]
  We propose a decentralized FL framework by integrating blockchain into FL, namely, blockchain assisted decentralized federated learning (BLADE-FL) In a round of the proposed BLADE-FL, each client broadcasts its trained model to other clients, competes to generate a block based on the received models, and then aggregates the models from the generated block before its local training of the next round. We explore the impact of lazy clients on the learning performance of BLADE-FL, and characterize the relationship among the optimal K, the learning parameters, and the proportion of lazy clients.
  arXiv  Detail & Related papers  (2021-01-18T07:19:08Z)

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.