Empowering Federated Learning with Implicit Gossiping: Mitigating Connection Unreliability Amidst Unknown and Arbitrary Dynamics
- URL: http://arxiv.org/abs/2404.10091v1
- Date: Mon, 15 Apr 2024 18:58:39 GMT
- Title: Empowering Federated Learning with Implicit Gossiping: Mitigating Connection Unreliability Amidst Unknown and Arbitrary Dynamics
- Authors: Ming Xiang, Stratis Ioannidis, Edmund Yeh, Carlee Joe-Wong, Lili Su,
- Abstract summary: Federated learning is a popular approach for training a machine learning model without disclosing raw data.
We show that when the $p_it$'s vary across clients, the most widely adopted federated learning algorithm, Federated Average (FedAvg), experiences significant bias.
We propose Federated Postponed Broadcast (FedPBC), a simple variant of FedAvg.
- Score: 23.466997173249034
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Federated learning is a popular distributed learning approach for training a machine learning model without disclosing raw data. It consists of a parameter server and a possibly large collection of clients (e.g., in cross-device federated learning) that may operate in congested and changing environments. In this paper, we study federated learning in the presence of stochastic and dynamic communication failures wherein the uplink between the parameter server and client $i$ is on with unknown probability $p_i^t$ in round $t$. Furthermore, we allow the dynamics of $p_i^t$ to be arbitrary. We first demonstrate that when the $p_i^t$'s vary across clients, the most widely adopted federated learning algorithm, Federated Average (FedAvg), experiences significant bias. To address this observation, we propose Federated Postponed Broadcast (FedPBC), a simple variant of FedAvg. FedPBC differs from FedAvg in that the parameter server postpones broadcasting the global model till the end of each round. Despite uplink failures, we show that FedPBC converges to a stationary point of the original non-convex objective. On the technical front, postponing the global model broadcasts enables implicit gossiping among the clients with active links in round $t$. Despite the time-varying nature of $p_i^t$, we can bound the perturbation of the global model dynamics using techniques to control gossip-type information mixing errors. Extensive experiments have been conducted on real-world datasets over diversified unreliable uplink patterns to corroborate our analysis.
Related papers
- Robust Model Evaluation over Large-scale Federated Networks [8.700087812420687]
We address the challenge of certifying the performance of a machine learning model on an unseen target network.
We derive theoretical guarantees for the model's empirical average loss and provide uniform bounds on the risk CDF.
Our bounds are computable in time with a number of queries to the $K$ clients, preserving client privacy by querying only the model's loss on private data.
arXiv Detail & Related papers (2024-10-26T18:45:15Z) - Personalized federated learning based on feature fusion [2.943623084019036]
Federated learning enables distributed clients to collaborate on training while storing their data locally to protect client privacy.
We propose a personalized federated learning approach called pFedPM.
In our process, we replace traditional gradient uploading with feature uploading, which helps reduce communication costs and allows for heterogeneous client models.
arXiv Detail & Related papers (2024-06-24T12:16:51Z) - Towards Bias Correction of FedAvg over Nonuniform and Time-Varying
Communications [26.597515045714502]
Federated learning (FL) is a decentralized learning framework wherein a parameter server (PS) and a collection of clients collaboratively train a model via a global objective.
We show that when the channel conditions are heterogeneous across clients are changing over time, the FedFederated Postponed global model fails to postpone the gossip-type information mixing errors.
arXiv Detail & Related papers (2023-06-01T01:52:03Z) - Beyond ADMM: A Unified Client-variance-reduced Adaptive Federated
Learning Framework [82.36466358313025]
We propose a primal-dual FL algorithm, termed FedVRA, that allows one to adaptively control the variance-reduction level and biasness of the global model.
Experiments based on (semi-supervised) image classification tasks demonstrate superiority of FedVRA over the existing schemes.
arXiv Detail & Related papers (2022-12-03T03:27:51Z) - DYNAFED: Tackling Client Data Heterogeneity with Global Dynamics [60.60173139258481]
Local training on non-iid distributed data results in deflected local optimum.
A natural solution is to gather all client data onto the server, such that the server has a global view of the entire data distribution.
In this paper, we put forth an idea to collect and leverage global knowledge on the server without hindering data privacy.
arXiv Detail & Related papers (2022-11-20T06:13:06Z) - Meta Knowledge Condensation for Federated Learning [65.20774786251683]
Existing federated learning paradigms usually extensively exchange distributed models at a central solver to achieve a more powerful model.
This would incur severe communication burden between a server and multiple clients especially when data distributions are heterogeneous.
Unlike existing paradigms, we introduce an alternative perspective to significantly decrease the communication cost in federate learning.
arXiv Detail & Related papers (2022-09-29T15:07:37Z) - Federated Stochastic Approximation under Markov Noise and Heterogeneity: Applications in Reinforcement Learning [24.567125948995834]
Federated reinforcement learning is a framework in which $N$ agents collaboratively learn a global model.
We show that by careful collaboration of the agents in solving this joint fixed point problem, we can find the global model $N$ times faster.
arXiv Detail & Related papers (2022-06-21T08:39:12Z) - FedAvg with Fine Tuning: Local Updates Lead to Representation Learning [54.65133770989836]
Federated Averaging (FedAvg) algorithm consists of alternating between a few local gradient updates at client nodes, followed by a model averaging update at the server.
We show that the reason behind generalizability of the FedAvg's output is its power in learning the common data representation among the clients' tasks.
We also provide empirical evidence demonstrating FedAvg's representation learning ability in federated image classification with heterogeneous data.
arXiv Detail & Related papers (2022-05-27T00:55:24Z) - An Expectation-Maximization Perspective on Federated Learning [75.67515842938299]
Federated learning describes the distributed training of models across multiple clients while keeping the data private on-device.
In this work, we view the server-orchestrated federated learning process as a hierarchical latent variable model where the server provides the parameters of a prior distribution over the client-specific model parameters.
We show that with simple Gaussian priors and a hard version of the well known Expectation-Maximization (EM) algorithm, learning in such a model corresponds to FedAvg, the most popular algorithm for the federated learning setting.
arXiv Detail & Related papers (2021-11-19T12:58:59Z) - 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) - Adversarial Robustness through Bias Variance Decomposition: A New
Perspective for Federated Learning [41.525434598682764]
Federated learning learns a neural network model by aggregating the knowledge from a group of distributed clients under the privacy-preserving constraint.
We show that this paradigm might inherit the adversarial vulnerability of the centralized neural network.
We propose an adversarially robust federated learning framework, named Fed_BVA, with improved server and client update mechanisms.
arXiv Detail & Related papers (2020-09-18T18:58:25Z)
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.