Distributed Federated Learning by Alternating Periods of Training

• URL: http://arxiv.org/abs/2601.01793v1
• Date: Mon, 05 Jan 2026 05:06:58 GMT
• Title: Distributed Federated Learning by Alternating Periods of Training
• Authors: Shamik Bhattacharyya, Rachel Kalpana Kalaimani,
• Abstract summary: Federated learning is a privacy-focused approach towards machine learning where models are trained on client devices with locally available data and aggregated at a central server.<n>We present a distributed approach to federated learning comprising multiple servers with inter-server communication capabilities.<n>We propose a novel DFL (Distributed Federated Learning) algorithm which uses alternating periods of local training on the client data followed by global training among servers.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated learning is a privacy-focused approach towards machine learning where models are trained on client devices with locally available data and aggregated at a central server. However, the dependence on a single central server is challenging in the case of a large number of clients and even poses the risk of a single point of failure. To address these critical limitations of scalability and fault-tolerance, we present a distributed approach to federated learning comprising multiple servers with inter-server communication capabilities. While providing a fully decentralized approach, the designed framework retains the core federated learning structure where each server is associated with a disjoint set of clients with server-client communication capabilities. We propose a novel DFL (Distributed Federated Learning) algorithm which uses alternating periods of local training on the client data followed by global training among servers. We show that the DFL algorithm, under a suitable choice of parameters, ensures that all the servers converge to a common model value within a small tolerance of the ideal model, thus exhibiting effective integration of local and global training models. Finally, we illustrate our theoretical claims through numerical simulations.

Related papers

• TRAIL: Trust-Aware Client Scheduling for Semi-Decentralized Federated Learning [13.144501509175985]
  We propose a TRust-Aware clIent scheduLing mechanism called TRAIL, which assesses client states and contributions.<n>We focus on a semi-decentralized FL framework where edge servers and clients train a shared global model using unreliable intra-cluster model aggregation and inter-cluster model consensus.<n>Experiments conducted on real-world datasets demonstrate that TRAIL outperforms state-of-the-art baselines, achieving an improvement of 8.7% in test accuracy and a reduction of 15.3% in training loss.
  arXiv  Detail & Related papers  (2024-12-16T05:02:50Z)
• FedSPD: A Soft-clustering Approach for Personalized Decentralized Federated Learning [23.140777064095833]
  Federated learning is a framework for distributed clients to collaboratively train a machine learning model using local data.<n>We propose FedSPD, an efficient personalized federated learning algorithm for the decentralized setting.<n>We show that FedSPD learns accurate models even in low-connectivity networks.
  arXiv  Detail & Related papers  (2024-10-24T15:48:34Z)
• Scheduling and Communication Schemes for Decentralized Federated Learning [0.31410859223862103]
  A decentralized federated learning (DFL) model with the gradient descent (SGD) algorithm has been introduced. Three scheduling policies for DFL have been proposed for communications between the clients and the parallel servers. Results show that the proposed scheduling polices have an impact both on the speed of convergence and in the final global model.
  arXiv  Detail & Related papers  (2023-11-27T17:35:28Z)
• Scalable Collaborative Learning via Representation Sharing [53.047460465980144]
  Federated learning (FL) and Split Learning (SL) are two frameworks that enable collaborative learning while keeping the data private (on device) In FL, each data holder trains a model locally and releases it to a central server for aggregation. In SL, the clients must release individual cut-layer activations (smashed data) to the server and wait for its response (during both inference and back propagation). In this work, we present a novel approach for privacy-preserving machine learning, where the clients collaborate via online knowledge distillation using a contrastive loss.
  arXiv  Detail & Related papers  (2022-11-20T10:49:22Z)
• Optimizing Server-side Aggregation For Robust Federated Learning via Subspace Training [80.03567604524268]
  Non-IID data distribution across clients and poisoning attacks are two main challenges in real-world federated learning systems. We propose SmartFL, a generic approach that optimize the server-side aggregation process. We provide theoretical analyses of the convergence and generalization capacity for SmartFL.
  arXiv  Detail & Related papers  (2022-11-10T13:20:56Z)
• 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)
• Server Averaging for Federated Learning [14.846231685735592]
  Federated learning allows distributed devices to collectively train a model without sharing or disclosing the local dataset with a central server. The improved privacy of federated learning also introduces challenges including higher computation and communication costs. We propose the server averaging algorithm to accelerate convergence.
  arXiv  Detail & Related papers  (2021-03-22T07:07:00Z)
• A Bayesian Federated Learning Framework with Online Laplace Approximation [144.7345013348257]
  Federated learning allows multiple clients to collaboratively learn a globally shared model. We propose a novel FL framework that uses online Laplace approximation to approximate posteriors on both the client and server side. We achieve state-of-the-art results on several benchmarks, clearly demonstrating the advantages of the proposed method.
  arXiv  Detail & Related papers  (2021-02-03T08:36:58Z)
• Decentralised Learning from Independent Multi-Domain Labels for Person Re-Identification [69.29602103582782]
  Deep learning has been successful for many computer vision tasks due to the availability of shared and centralised large-scale training data. However, increasing awareness of privacy concerns poses new challenges to deep learning, especially for person re-identification (Re-ID) We propose a novel paradigm called Federated Person Re-Identification (FedReID) to construct a generalisable global model (a central server) by simultaneously learning with multiple privacy-preserved local models (local clients) This client-server collaborative learning process is iteratively performed under privacy control, enabling FedReID to realise decentralised learning without sharing distributed data nor collecting any
  arXiv  Detail & Related papers  (2020-06-07T13:32:33Z)
• Federated Residual Learning [53.77128418049985]
  We study a new form of federated learning where the clients train personalized local models and make predictions jointly with the server-side shared model. Using this new federated learning framework, the complexity of the central shared model can be minimized while still gaining all the performance benefits that joint training provides.
  arXiv  Detail & Related papers  (2020-03-28T19:55:24Z)
• Coded Federated Learning [5.375775284252717]
  Federated learning is a method of training a global model from decentralized data distributed across client devices. Our results show that CFL allows the global model to converge nearly four times faster when compared to an uncoded approach.
  arXiv  Detail & Related papers  (2020-02-21T23:06:20Z)

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.