Decentralized Online Learning: Take Benefits from Others' Data without Sharing Your Own to Track Global Trend

Zhao, Yawei; Zhao, Peilin; Tang, Hanlin; Qiu, Shuang; Liu, Ji

doi:10.48550/arxiv.1901.10593

Cited by 10 publications

(22 citation statements)

References 31 publications

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“…The communication model includes delays, and the regret bound depends on a quantity related to the mixing time of a certain random walk on the network. Zhao et al [18] study a decentralized online learning setting in which losses are characterized by two components, one adversarial and another stochastic. They show upper bounds on the regret in terms of a constant representing the magnitude of the adversarial component and another constant measuring the randomness of the stochastic part.…”

Section: Related Workmentioning

confidence: 99%

Cooperative Online Learning: Keeping your Neighbors Updated

Cesa-Bianchi,

Cesari,

Monteleoni

2019

Preprint

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We study an asynchronous online learning setting with a network of agents. At each time step, some of the agents are activated, requested to make a prediction, and pay the corresponding loss. The loss function is then revealed to these agents and also to their neighbors in the network. Our results characterize how much knowing the network structure affects the regret as a function of the model of agent activations. When activations are stochastic, the optimal regret (up to constant factors) is shown to be of order √ αT , where T is the horizon and α is the independence number of the network. We prove that the upper bound is achieved even when agents have no information about the network structure. When activations are adversarial the situation changes dramatically: if agents ignore the network structure, a Ω(T ) lower bound on the regret can be proven, showing that learning is impossible. However, when agents can choose to ignore some of their neighbors based on the knowledge of the network structure, we prove a O( √ χT ) sublinear regret bound, where χ ≥ α is the clique-covering number of the network.

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Section: Related Workmentioning

confidence: 99%

Cooperative Online Learning: Keeping your Neighbors Updated

Cesa-Bianchi,

Cesari,

Monteleoni

2019

Preprint

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“…Examples of these works include Kamp et al (2014); Shahrampour and Jadbabaie (2017); Lee et al (2016). Notably, Zhao et al (2019) shares similar problem definition and theoretical result as our paper. However, single sided communication is not allowed in their setting, making their results more restrictive.…”

Section: Related Workmentioning

confidence: 52%

“…t for any i and t, then Algorithm 1 reduces to the distributed online gradient method proposed by Zhao et al (2019).…”

mentioning

confidence: 99%

Central Server Free Federated Learning over Single-sided Trust Social Networks

He,

Tan,

Tang

et al. 2019

Preprint

Self Cite

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Federated learning has become increasingly important for modern machine learning, especially for data privacy-sensitive scenarios. Existing federated learning mostly adopts the central server-based architecture or centralized architecture. However, in many social network scenarios, centralized federated learning is not applicable (e.g., a central agent or server connecting all users may not exist, or the communication cost to the central server is not affordable). In this paper, we consider a generic setting: 1) the central server may not exist, and 2) the social network is unidirectional or of single-sided trust (i.e., user A trusts user B but user B may not trust user A). We propose a central server free federated learning algorithm, named Online Push-Sum (OPS) method, to handle this challenging but generic scenario. A rigorous regret analysis is also provided, which shows very interesting results on how users can benefit from communication with trusted users in the federated learning scenario. This work builds upon the fundamental algorithm framework and theoretical guarantees for federated learning in the generic social network scenario.

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“…However, little attention has been paid to understand the impact of the network size on the average regret achievable at each individual agent therein. To reap the potential benefits that an agent can achieve when carrying out distributed OCO, a convex loss function with both adversarial and stochastic components is considered in [36]. Assuming that the expected gradient is bounded above by 𝐺 and the stochastic variance is bounded above by 𝜎 2 , they have shown that the network expected regret is 𝑂 ( √ 𝑁 2 𝐺 2 𝑇 + 𝑁𝑇 𝜎 2 ).…”

Section: Related Workmentioning

confidence: 99%

“…Remark 2. The classical DOGD algorithm [36] cannot achieve such performance gain in the setting here, because essentially DOGD performs a consensus step followed by a gradient descent along the local gradient ∇𝑓 𝑡,𝑖 (𝑥 𝑡,𝑖 ). For a fixed learning rate, DOGD only converges to a neighborhood of the optimizer 𝑥 * , because the local gradient is data-driven and hence random.…”

Section: Performance Analysismentioning

confidence: 99%

Accelerating Distributed Online Meta-Learning via Multi-Agent Collaboration under Limited Communication

Lin

Dedeoğlu

Zhang

2020

Preprint

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Online meta-learning is emerging as an enabling technique for achieving edge intelligence in the IoT ecosystem. Nevertheless, to learn a good meta-model for within-task fast adaptation, a single agent alone has to learn over many tasks, and this is the so-called 'cold-start' problem. Observing that in a multi-agent network the learning tasks across different agents often share some model similarity, we ask the following fundamental question: "Is it possible to accelerate the online meta-learning across agents via limited communication and if yes how much benefit can be achieved? " To answer this question, we propose a multi-agent online metalearning framework and cast it as an equivalent two-level nested online convex optimization (OCO) problem. By characterizing the upper bound of the agent-task-averaged regret, we show that the performance of multi-agent online meta-learning depends heavily on how much an agent can benefit from the distributed networklevel OCO for meta-model updates via limited communication, which however is not well understood. To tackle this challenge, we devise a distributed online gradient descent algorithm with gradient tracking where each agent tracks the global gradient using only one communication step with its neighbors per iteration, and it results in an average regret 𝑂 ( √︁ 𝑇 /𝑁 ) per agent, indicating that a factor of √︁ 1/𝑁 speedup over the optimal single-agent regret 𝑂 ( √ 𝑇 ) after 𝑇 iterations, where 𝑁 is the number of agents. Building on this sharp performance speedup, we next develop a multi-agent online meta-learning algorithm and show that it can achieve the optimal task-average regret at a faster rate of 𝑂 (1/ √ 𝑁𝑇 ) via limited communication, compared to single-agent online meta-learning. Extensive experiments corroborate the theoretic results.

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Decentralized Online Learning: Take Benefits from Others' Data without Sharing Your Own to Track Global Trend

Cited by 10 publications

References 31 publications

Cooperative Online Learning: Keeping your Neighbors Updated

Cooperative Online Learning: Keeping your Neighbors Updated

Central Server Free Federated Learning over Single-sided Trust Social Networks

Accelerating Distributed Online Meta-Learning via Multi-Agent Collaboration under Limited Communication

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