7,819 research outputs found
Robust federated learning with noisy communication
Federated learning is a communication-efficient training process that alternate between local training at the edge devices and averaging of the updated local model at the center server. Nevertheless, it is impractical to achieve perfect acquisition of the local models in wireless communication due to the noise, which also brings serious effect on federated learning. To tackle this challenge in this paper, we propose a robust design for federated learning to decline the effect of noise. Considering the noise in two aforementioned steps, we first formulate the training problem as a parallel optimization for each node under the expectation-based model and worst-case model. Due to the non-convexity of the problem, regularizer approximation method is proposed to make it tractable. Regarding the worst-case model, we utilize the sampling-based successive convex approximation algorithm to develop a feasible training scheme to tackle the unavailable maxima or minima noise condition and the non-convex issue of the objective function. Furthermore, the convergence rates of both new designs are analyzed from a theoretical point of view. Finally, the improvement of prediction accuracy and the reduction of loss function value are demonstrated via simulation for the proposed designs
Continual Local Training for Better Initialization of Federated Models
Federated learning (FL) refers to the learning paradigm that trains machine
learning models directly in the decentralized systems consisting of smart edge
devices without transmitting the raw data, which avoids the heavy communication
costs and privacy concerns. Given the typical heterogeneous data distributions
in such situations, the popular FL algorithm \emph{Federated Averaging}
(FedAvg) suffers from weight divergence and thus cannot achieve a competitive
performance for the global model (denoted as the \emph{initial performance} in
FL) compared to centralized methods. In this paper, we propose the local
continual training strategy to address this problem. Importance weights are
evaluated on a small proxy dataset on the central server and then used to
constrain the local training. With this additional term, we alleviate the
weight divergence and continually integrate the knowledge on different local
clients into the global model, which ensures a better generalization ability.
Experiments on various FL settings demonstrate that our method significantly
improves the initial performance of federated models with few extra
communication costs.Comment: This paper has been accepted to 2020 IEEE International Conference on
Image Processing (ICIP 2020
Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge
We envision a mobile edge computing (MEC) framework for machine learning (ML)
technologies, which leverages distributed client data and computation resources
for training high-performance ML models while preserving client privacy. Toward
this future goal, this work aims to extend Federated Learning (FL), a
decentralized learning framework that enables privacy-preserving training of
models, to work with heterogeneous clients in a practical cellular network. The
FL protocol iteratively asks random clients to download a trainable model from
a server, update it with own data, and upload the updated model to the server,
while asking the server to aggregate multiple client updates to further improve
the model. While clients in this protocol are free from disclosing own private
data, the overall training process can become inefficient when some clients are
with limited computational resources (i.e. requiring longer update time) or
under poor wireless channel conditions (longer upload time). Our new FL
protocol, which we refer to as FedCS, mitigates this problem and performs FL
efficiently while actively managing clients based on their resource conditions.
Specifically, FedCS solves a client selection problem with resource
constraints, which allows the server to aggregate as many client updates as
possible and to accelerate performance improvement in ML models. We conducted
an experimental evaluation using publicly-available large-scale image datasets
to train deep neural networks on MEC environment simulations. The experimental
results show that FedCS is able to complete its training process in a
significantly shorter time compared to the original FL protocol
Federated Neural Architecture Search
To preserve user privacy while enabling mobile intelligence, techniques have
been proposed to train deep neural networks on decentralized data. However,
training over decentralized data makes the design of neural architecture quite
difficult as it already was. Such difficulty is further amplified when
designing and deploying different neural architectures for heterogeneous mobile
platforms. In this work, we propose an automatic neural architecture search
into the decentralized training, as a new DNN training paradigm called
Federated Neural Architecture Search, namely federated NAS. To deal with the
primary challenge of limited on-client computational and communication
resources, we present FedNAS, a highly optimized framework for efficient
federated NAS. FedNAS fully exploits the key opportunity of insufficient model
candidate re-training during the architecture search process, and incorporates
three key optimizations: parallel candidates training on partial clients, early
dropping candidates with inferior performance, and dynamic round numbers.
Tested on large-scale datasets and typical CNN architectures, FedNAS achieves
comparable model accuracy as state-of-the-art NAS algorithm that trains models
with centralized data, and also reduces the client cost by up to two orders of
magnitude compared to a straightforward design of federated NAS
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