Parallel Successive Learning for Dynamic Distributed Model Training over Heterogeneous Wireless Networks

• URL: http://arxiv.org/abs/2202.02947v6
• Date: Wed, 14 Jun 2023 16:50:03 GMT
• Title: Parallel Successive Learning for Dynamic Distributed Model Training over Heterogeneous Wireless Networks
• Authors: Seyyedali Hosseinalipour, Su Wang, Nicolo Michelusi, Vaneet Aggarwal, Christopher G. Brinton, David J. Love, Mung Chiang
• Abstract summary: Federated learning (FedL) has emerged as a popular technique for distributing model training over a set of wireless devices. We develop parallel successive learning (PSL), which expands the FedL architecture along three dimensions. Our analysis sheds light on the notion of cold vs. warmed up models, and model inertia in distributed machine learning.
• Score: 50.68446003616802
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated learning (FedL) has emerged as a popular technique for distributing model training over a set of wireless devices, via iterative local updates (at devices) and global aggregations (at the server). In this paper, we develop parallel successive learning (PSL), which expands the FedL architecture along three dimensions: (i) Network, allowing decentralized cooperation among the devices via device-to-device (D2D) communications. (ii) Heterogeneity, interpreted at three levels: (ii-a) Learning: PSL considers heterogeneous number of stochastic gradient descent iterations with different mini-batch sizes at the devices; (ii-b) Data: PSL presumes a dynamic environment with data arrival and departure, where the distributions of local datasets evolve over time, captured via a new metric for model/concept drift. (ii-c) Device: PSL considers devices with different computation and communication capabilities. (iii) Proximity, where devices have different distances to each other and the access point. PSL considers the realistic scenario where global aggregations are conducted with idle times in-between them for resource efficiency improvements, and incorporates data dispersion and model dispersion with local model condensation into FedL. Our analysis sheds light on the notion of cold vs. warmed up models, and model inertia in distributed machine learning. We then propose network-aware dynamic model tracking to optimize the model learning vs. resource efficiency tradeoff, which we show is an NP-hard signomial programming problem. We finally solve this problem through proposing a general optimization solver. Our numerical results reveal new findings on the interdependencies between the idle times in-between the global aggregations, model/concept drift, and D2D cooperation configuration.

Related papers

• Federated Learning with MMD-based Early Stopping for Adaptive GNSS Interference Classification [4.674584508653125]
  Federated learning (FL) enables multiple devices to collaboratively train a global model while maintaining data on local servers. We propose an FL approach using few-shot learning and aggregation of the model weights on a global server. An exemplary application of FL is orchestrating machine learning models along highways for interference classification based on snapshots from global navigation satellite system (GNSS) receivers.
  arXiv  Detail & Related papers  (2024-10-21T06:43:04Z)
• Stragglers-Aware Low-Latency Synchronous Federated Learning via Layer-Wise Model Updates [71.81037644563217]
  Synchronous federated learning (FL) is a popular paradigm for collaborative edge learning. As some of the devices may have limited computational resources and varying availability, FL latency is highly sensitive to stragglers. We propose straggler-aware layer-wise federated learning (SALF) that leverages the optimization procedure of NNs via backpropagation to update the global model in a layer-wise fashion.
  arXiv  Detail & Related papers  (2024-03-27T09:14:36Z)
• Device Sampling and Resource Optimization for Federated Learning in Cooperative Edge Networks [17.637761046608]
  Federated learning (FedL) distributes machine learning (ML) across worker devices by having them train local models that are periodically aggregated by a server. FedL ignores two important characteristics of contemporary wireless networks: (i) the network may contain heterogeneous communication/computation resources, and (ii) there may be significant overlaps in devices' local data distributions. We develop a novel optimization methodology that jointly accounts for these factors via intelligent device sampling complemented by device-to-device (D2D) offloading.
  arXiv  Detail & Related papers  (2023-11-07T21:17:59Z)
• Over-the-Air Federated Learning and Optimization [52.5188988624998]
  We focus on Federated learning (FL) via edge-the-air computation (AirComp) We describe the convergence of AirComp-based FedAvg (AirFedAvg) algorithms under both convex and non- convex settings. For different types of local updates that can be transmitted by edge devices (i.e., model, gradient, model difference), we reveal that transmitting in AirFedAvg may cause an aggregation error. In addition, we consider more practical signal processing schemes to improve the communication efficiency and extend the convergence analysis to different forms of model aggregation error caused by these signal processing schemes.
  arXiv  Detail & Related papers  (2023-10-16T05:49:28Z)
• Asynchronous Multi-Model Dynamic Federated Learning over Wireless Networks: Theory, Modeling, and Optimization [20.741776617129208]
  Federated learning (FL) has emerged as a key technique for distributed machine learning (ML) We first formulate rectangular scheduling steps and functions to capture the impact of system parameters on learning performance. Our analysis sheds light on the joint impact of device training variables and asynchronous scheduling decisions.
  arXiv  Detail & Related papers  (2023-05-22T21:39:38Z)
• Multi-Edge Server-Assisted Dynamic Federated Learning with an Optimized Floating Aggregation Point [51.47520726446029]
  cooperative edge learning (CE-FL) is a distributed machine learning architecture. We model the processes taken during CE-FL, and conduct analytical training. We show the effectiveness of our framework with the data collected from a real-world testbed.
  arXiv  Detail & Related papers  (2022-03-26T00:41:57Z)
• Two Timescale Hybrid Federated Learning with Cooperative D2D Local Model Aggregations [10.702853653891902]
  Federated learning has emerged as a popular technique for distributing machine learning (ML) model training across the wireless edge. We propose two timescale hybrid federated learning (TT-HF), which is a hybrid between the device-to-server communication paradigm in federated learning and device-to-device (D2D) communications for model training.
  arXiv  Detail & Related papers  (2021-03-18T18:58:45Z)
• Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation [24.084053136210027]
  We develop a sampling methodology based on graph sequential convolutional networks (GCNs) We find that our methodology while sampling less than 5% of all devices outperforms conventional federated learning (FedL) substantially both in terms of trained model accuracy and required resource utilization.
  arXiv  Detail & Related papers  (2021-01-04T05:59:50Z)
• Fast-Convergent Federated Learning [82.32029953209542]
  Federated learning is a promising solution for distributing machine learning tasks through modern networks of mobile devices. We propose a fast-convergent federated learning algorithm, called FOLB, which performs intelligent sampling of devices in each round of model training.
  arXiv  Detail & Related papers  (2020-07-26T14:37:51Z)
• Federated Learning With Quantized Global Model Updates [84.55126371346452]
  We study federated learning, which enables mobile devices to utilize their local datasets to train a global model. We introduce a lossy FL (LFL) algorithm, in which both the global model and the local model updates are quantized before being transmitted.
  arXiv  Detail & Related papers  (2020-06-18T16:55:20Z)

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.