3 research outputs found
Privacy-preserving Incremental ADMM for Decentralized Consensus Optimization
The alternating direction method of multipliers (ADMM) has been recently
recognized as a promising optimizer for large-scale machine learning models.
However, there are very few results studying ADMM from the aspect of
communication costs, especially jointly with privacy preservation, which are
critical for distributed learning. We investigate the communication efficiency
and privacy-preservation of ADMM by solving the consensus optimization problem
over decentralized networks. Since walk algorithms can reduce communication
load, we first propose incremental ADMM (I-ADMM) based on the walk algorithm,
the updating order of which follows a Hamiltonian cycle instead. However,
I-ADMM cannot guarantee the privacy for agents against external eavesdroppers
even if the randomized initialization is applied. To protect privacy for
agents, we then propose two privacy-preserving incremental ADMM algorithms,
i.e., PI-ADMM1 and PI-ADMM2, where perturbation over step sizes and primal
variables is adopted, respectively. Through theoretical analyses, we prove the
convergence and privacy preservation for PI-ADMM1, which are further supported
by numerical experiments. Besides, simulations demonstrate that the proposed
PI-ADMM1 and PI-ADMM2 algorithms are communication efficient compared with
state-of-the-art methods
Fully Decentralized Federated Learning Based Beamforming Design for UAV Communications
To handle the data explosion in the era of internet of things (IoT), it is of
interest to investigate the decentralized network, with the aim at relaxing the
burden to central server along with keeping data privacy. In this work, we
develop a fully decentralized federated learning (FL) framework with an inexact
stochastic parallel random walk alternating direction method of multipliers
(ISPW-ADMM). Performing more communication efficient and enhanced privacy
preservation compared with the current state-of-the-art, the proposed ISPW-ADMM
can be partially immune to the impacts from time-varying dynamic network and
stochastic data collection, while still in fast convergence. Benefits from the
stochastic gradients and biased first-order moment estimation, the proposed
framework can be applied to any decentralized FL tasks over time-varying
graphs. Thus to further demonstrate the practicability of such framework in
providing fast convergence, high communication efficiency, and system
robustness, we study the extreme learning machine(ELM)-based FL model for
robust beamforming (BF) design in UAV communications, as verified by the
numerical simulations
Adaptive Stochastic ADMM for Decentralized Reinforcement Learning in Edge Industrial IoT
Edge computing provides a promising paradigm to support the implementation of
Industrial Internet of Things (IIoT) by offloading tasks to nearby edge nodes.
Meanwhile, the increasing network size makes it impractical for centralized
data processing due to limited bandwidth, and consequently a decentralized
learning scheme is preferable. Reinforcement learning (RL) has been widely
investigated and shown to be a promising solution for decision-making and
optimal control processes. For RL in a decentralized setup, edge nodes (agents)
connected through a communication network aim to work collaboratively to find a
policy to optimize the global reward as the sum of local rewards. However,
communication costs, scalability and adaptation in complex environments with
heterogeneous agents may significantly limit the performance of decentralized
RL. Alternating direction method of multipliers (ADMM) has a structure that
allows for decentralized implementation, and has shown faster convergence than
gradient descent based methods. Therefore, we propose an adaptive stochastic
incremental ADMM (asI-ADMM) algorithm and apply the asI-ADMM to decentralized
RL with edge-computing-empowered IIoT networks. We provide convergence
properties for proposed algorithms by designing a Lyapunov function and prove
that the asI-ADMM has convergence rate where
and are the number of iterations and batch samples, respectively.
Then, we test our algorithm with two supervised learning problems. For
performance evaluation, we simulate two applications in decentralized RL
settings with homogeneous and heterogeneous agents. The experiment results show
that our proposed algorithms outperform the state of the art in terms of
communication costs and scalability, and can well adapt to complex IoT
environments