In this paper, it is shown, for the first time, that centralized performance is achievable in decentralized learning without sharing the local datasets. Specifically, when clients adopt an empirical risk minimization with relative-entropy regularization (ERM-RER) learning framework and a forward-backward communication between clients is established, it suffices to share the locally obtained Gibbs measures to achieve the same performance as that of a centralized ERM-RER with access to all the datasets. The core idea is that the Gibbs measure produced by client~$k$ is used, as reference measure, by client~$k+1$. This effectively establishes a principled way to encode prior information through a reference measure. In particular, achieving centralized performance in the decentralized setting requires a specific scaling of the regularization factors with the local sample sizes. Overall, this result opens the door to novel decentralized learning paradigms that shift the collaboration strategy from sharing data to sharing the local inductive bias via the reference measures over the set of models.

We consider decentralized machine learning over a network where the training data is distributed across $n$ agents, each of which can compute stochastic model updates on their local data. The agent's common goal is to find a model that minimizes the average of all local loss functions. While gradient tracking (GT) algorithms can overcome a key challenge, namely accounting for differences between workers' local data distributions, the known convergence rates for GT algorithms are not optimal with respect to their dependence on the mixing parameter $p$ (related to the spectral gap of the connectivity matrix).We provide a tighter analysis of the GT method in the stochastic strongly convex, convex and non-convex settings. We improve the dependency on $p$ from $\mathcal{O}(p^{-2})$ to $\mathcal{O}(p^{-1}c^{-1})$ in the noiseless case and from $\mathcal{O}(p^{-3/2})$ to $\mathcal{O}(p^{-1/2}c^{-1})$ in the general stochastic case, where $c \geq p$ is related to the negative eigenvalues of the connectivity matrix (and is a constant in most practical applications). This improvement was possible due to a new proof technique which could be of independent interest.

We consider decentralized machine learning over a network where the training data is distributed across n agents, each of which can compute stochastic model updates on their local data. The agent's common goal is to find a model that minimizes the average of all local loss functions. While gradient tracking (GT) algorithms can overcome a key challenge, namely accounting for differences between workers' local data distributions, the known convergence rates for GT algorithms are not optimal with respect to their dependence on the mixing parameter p (related to the spectral gap of the connectivity matrix).We provide a tighter analysis of the GT method in the stochastic strongly convex, convex and non-convex settings. We improve the dependency on p from \mathcal{O}(p {-2}) to \mathcal{O}(p {-1}c {-1}) in the noiseless case and from \mathcal{O}(p {-3/2}) to \mathcal{O}(p {-1/2}c {-1}) in the general stochastic case, where c \geq p is related to the negative eigenvalues of the connectivity matrix (and is a constant in most practical applications). This improvement was possible due to a new proof technique which could be of independent interest.

We consider decentralized machine learning over a network where the training data is distributed across n agents, each of which can compute stochastic model updates on their local data. The agent's common goal is to find a model that minimizes the average of all local loss functions. While gradient tracking (GT) algorithms can overcome a key challenge, namely accounting for differences between workers' local data distributions, the known convergence rates for GT algorithms are not optimal with respect to their dependence on the mixing parameter p (related to the spectral gap of the connectivity matrix).We provide a tighter analysis of the GT method in the stochastic strongly convex, convex and non-convex settings. We improve the dependency on p from \mathcal{O}(p {-2}) to \mathcal{O}(p {-1}c {-1}) in the noiseless case and from \mathcal{O}(p {-3/2}) to \mathcal{O}(p {-1/2}c {-1}) in the general stochastic case, where c \geq p is related to the negative eigenvalues of the connectivity matrix (and is a constant in most practical applications). This improvement was possible due to a new proof technique which could be of independent interest.

The introduction of electronic personal health records (EHR) enables nationwide information exchange and curation among different health care systems. However, the current EHR systems do not provide transparent means for diagnosis support, medical research or can utilize the omnipresent data produced by the personal medical devices. Besides, the EHR systems are centrally orchestrated, which could potentially lead to a single point of failure. Therefore, in this article, we explore novel approaches for decentralizing machine learning over distributed ledgers to create intelligent EHR systems that can utilize information from personal medical devices for improved knowledge extraction. Consequently, we proposed and evaluated a conceptual EHR to enable anonymous predictive analysis across multiple medical institutions. The evaluation results indicate that the decentralized EHR can be deployed over the computing continuum with reduced machine learning time of up to 60% and consensus latency of below 8 seconds.

Decentralized Machine Learning (CURRENCY:DML) traded down 3.1% against the U.S. dollar during the 24-hour period ending at 22:00 PM E.T. on …

Decentralized Machine Learning (CURRENCY:DML) traded 2.4% lower against the U.S. dollar during the 1-day period ending at 7:00 AM Eastern on February 23rd. One Decentralized Machine Learning token can now be purchased for about $0.0036 or 0.00000094 BTC on cryptocurrency exchanges including DDEX and IDEX. Over the last seven days, Decentralized Machine Learning has traded down 10.6% against the U.S. dollar. Decentralized Machine Learning has a total market cap of $229,511.00 Here's how similar cryptocurrencies have performed over the last 24 hours: Decentralized Machine Learning's launch date was March 9th, 2018.

In the attempt of democratizing machine learning, data scientists should have the possibility to train their models on data they do not necessarily own, nor see. A model that is privately trained should be verified and uniquely identified across its entire life cycle, from its random initialization to setting the optimal values of its parameters. How does blockchain allow all this?

DML protocol will apply on-device machine learning, blockchain and federated learning technologies. It unleashes untapped data usage without extraction and idle processing power for machine learning. Algorithms will be crowdsourced from a developer community through the marketplace resulting innovation from periphery.