ADAPTIVE MULTI-CLOUD ORCHESTRATION FOR MACHINE LEARNING WORKFLOWS USING FEDERATED LEARNING TECHNIQUES

Abstract

The invention provides an adaptive multi-cloud orchestration system for machine learning workflows, leveraging federated learning techniques to enhance data privacy, compliance, and operational efficiency. The system dynamically allocates resources across multiple cloud providers based on workload demands, real-time cost analysis, and performance metrics. By employing federated learning, it enables distributed model training without centralized data aggregation, ensuring compliance with privacy regulations. Additionally, adaptive feedback mechanisms optimize workflow performance, scalability, and reliability. The system integrates privacy-preserving methods and a compliance layer, making it suitable for industries requiring secure, compliant, and cost-effective machine learning deployments.

Specification

Description:The embodiments of the present invention generally relates to multi-cloud orchestration in machine learning workflows, specifically involving federated learning techniques. The invention focuses on adapting machine learning models across diverse cloud infrastructures, optimizing workflow allocation, cost management, and data security in response to dynamic cloud environments. It aims to enhance performance, regulatory compliance, and privacy in distributed machine learning by enabling seamless orchestration across multiple cloud providers.
BACKGROUND OF THE INVENTION
The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prior art.

In recent years, machine learning applications have rapidly evolved, demanding immense computational power, storage, and scalability. Cloud computing has become a preferred choice for handling these requirements due to its flexible infrastructure, elasticity, and pay-as-you-go models. However, as organizations scale up machine learning workloads, they often face challenges in managing costs, latency, and resource availability across different cloud platforms.

Using multi-cloud environments-utilizing multiple cloud providers simultaneously-has emerged as a viable solution, enabling organizations to balance costs, optimize latency, and increase redundancy. Nevertheless, coordinating machine learning workflows across these heterogeneous cloud environments is complex, requiring systems capable of dynamically orchestrating resources and adapting to changing conditions.

A prominent challenge in multi-cloud orchestration for machine learning is the management of data privacy and compliance across jurisdictions. Traditional centralized approaches for model training involve aggregating large volumes of data into a single location, raising concerns about data security and regulatory compliance. Federated learning has emerged as a promising technique to address these issues, as it allows distributed training of models without transferring raw data between locations. This enables data privacy by keeping sensitive data within localized environments while still contributing to global model improvements. However, federated learning, when combined with multi-cloud orchestration, introduces additional complexities, including maintaining consistent performance across clouds, handling data heterogeneity, and managing varied security protocols across regions.

The cost of deploying machine learning workflows across multiple clouds is a key consideration. Cloud providers offer different pricing models, which can fluctuate based on demand, location, and available resources. An efficient multi-cloud orchestration system must therefore be able to adaptively allocate resources and select cloud providers based on real-time cost and performance metrics to ensure cost efficiency without compromising workflow effectiveness. This requires sophisticated decision-making algorithms that continuously assess cloud options, manage costs, and respond to workload variations, further complicating the orchestration process.

Current orchestration solutions often lack the intelligence to adaptively manage machine learning workflows in federated learning environments, particularly in multi-cloud setups. These solutions may not adequately support real-time workload balancing, adaptive reallocation, or enforce data compliance requirements. Therefore, there is a need for an innovative orchestration framework that can seamlessly handle these challenges, enabling organizations to leverage the advantages of both multi-cloud computing and federated learning in a secure, compliant, and cost-effective manner.

OBJECTIVE OF THE INVENTION

Some of the objects of the present disclosure, which at least one embodiment herein satisfies are listed herein below.

An objective of the present invention is to develop an adaptive orchestration system for machine learning workflows across multiple cloud environments, utilizing federated learning techniques to enhance data privacy, compliance, and workflow efficiency.
An objective of the present invention is to provide a mechanism that continuously monitors and evaluates cloud resource performance, costs, and availability, dynamically selecting and reallocating resources based on changing conditions.

An objective of the present invention is to enable optimal workload distribution across multiple clouds while minimizing costs and latency.

An objective of the present invention is to integrate federated learning capabilities within the orchestration system, allowing distributed model training without centralized data aggregation. By keeping data local and decentralized, the system can enhance data security and ensure compliance with regional privacy regulations, such as the GDPR.

An objective of the present invention is to support data heterogeneity, enabling federated learning across diverse data sources and formats within multi-cloud environments.

An objective of the present invention is to create an adaptable framework that can respond to fluctuating demands and resource constraints, particularly in real-time. The system should dynamically reconfigure cloud resources based on workload changes, thereby maximizing the efficiency of machine learning workflows.

An objective of the present invention is to enable cost-effective multi-cloud orchestration by incorporating cost analysis tools that evaluate and compare cloud provider pricing models.

An objective of the present invention is to ensure efficient use of resources while maintaining budgetary constraints.

An objective of the present invention is to enhance data security and regulatory compliance by incorporating a compliance and security layer. This layer will enforce data residency requirements, manage access control, and monitor compliance with data protection laws, ensuring that workflows align with applicable regulations.

An objective of the present invention is to improve the overall scalability and resilience of machine learning workflows in multi-cloud environments. By providing a flexible and robust system for orchestrating machine learning tasks, the invention supports the deployment of large-scale, distributed workflows capable of withstanding resource failures or unexpected changes in cloud availability.

An objective of the present invention is to optimize model accuracy and performance by balancing data availability, resource utilization, and latency across clouds. By adapting orchestration based on real-time insights, the system can contribute to faster, more accurate model convergence, improving the quality and reliability of machine learning applications.

SUMMARY OF THE INVENTION
This section is provided to introduce certain objects and aspects of the present disclosure in a simplified form that are further described below in the detailed description. This summary is not intended to identify the key features or the scope of the claimed subject matter.

In an aspect, the invention presents an adaptive orchestration system for managing machine learning workflows across multiple cloud providers, leveraging federated learning techniques to address data privacy, compliance, and cost efficiency. The system comprises a cloud management layer that interfaces with various cloud providers to retrieve real-time performance and cost metrics, a federated learning module for distributed training across diverse cloud environments, and an orchestration engine that dynamically allocates resources based on adaptive algorithms. Additionally, a compliance and security layer enforces data privacy and regulatory requirements, ensuring that all operations comply with local and international standards.
By dynamically assessing resource availability, performance, and cost across clouds, the system adapts to changing demands and workload variations, optimizing cloud selection and resource allocation. The federated learning capabilities enable data security by eliminating the need for centralized data aggregation, while the adaptive orchestration ensures efficiency and scalability. This innovation provides a robust framework for deploying secure, compliant, and cost-effective machine learning workflows across multi-cloud environments.
BRIEF DESCRIPTION OF DRAWINGS
The accompanying drawings, which are incorporated herein, and constitute a part of this invention, illustrate exemplary embodiments of the disclosed methods and systems in which like reference numerals refer to the same parts throughout the different drawings. Components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present invention. Some drawings may indicate the components using block diagrams and may not represent the internal circuitry of each component. It will be appreciated by those skilled in the art that invention of such drawings includes the invention of electrical components, electronic components or circuitry commonly used to implement such components.

FIG. 1 illustrates an exemplary method for adaptive orchestration of machine learning workflows in a multi-cloud environment, in accordance with an embodiment of the present disclosure.

DETAILED DESCRIPTION OF THE INVENTION

In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address all of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein.

The ensuing description provides exemplary embodiments only and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.

Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail to avoid obscuring the embodiments.

Also, it is noted that individual embodiments may be described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.

The word "exemplary" and/or "demonstrative" is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as "exemplary" and/or "demonstrative" is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms "includes," "has," "contains," and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term "comprising" as an open transition word without precluding any additional or other elements.

Reference throughout this specification to "one embodiment" or "an embodiment" or "an instance" or "one instance" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.

The present invention provides a system and method for adaptive orchestration of machine learning workflows across multiple cloud environments using federated learning techniques. The system is designed to optimize resource allocation, enhance data security, and ensure compliance with regional regulations. The following sections describe three distinct embodiments of the invention, each tailored to address specific challenges in multi-cloud orchestration.

In first embodiment, the system focuses on dynamic resource allocation to manage federated learning workflows across multiple cloud providers. The system comprises:

A Cloud Management Layer that interfaces with cloud providers to retrieve resource availability, performance metrics, and cost data in real time.

An Orchestration Engine that uses adaptive algorithms to allocate resources dynamically. The engine evaluates workload requirements, such as computational intensity, data storage needs, and latency sensitivity, to determine the optimal resource distribution across clouds.

A Monitoring Module that continuously assesses resource usage and performance to predict potential bottlenecks or over-provisioning.

The workflow begins with the initialization of a federated learning task. Each participating node in the federated learning process operates on local data subsets, minimizing raw data transfer. The orchestration engine dynamically assigns computational tasks to the most suitable cloud resources based on predefined criteria, such as cost efficiency, proximity to data sources, and performance benchmarks. For instance, if a cloud provider's resources become costlier due to peak demand, the system reallocates tasks to a more economical provider without disrupting the workflow.

This embodiment ensures cost-effective and efficient resource utilization while maintaining high-performance standards for federated learning tasks. Additionally, it provides scalability, allowing the system to handle varying workloads across multiple regions seamlessly.

The second embodiment emphasizes data privacy and compliance with regulatory frameworks, such as GDPR and CCPA. This embodiment integrates a Compliance and Security Layer that ensures all data processing adheres to applicable laws and organizational policies.

The federated learning module in this embodiment employs privacy-preserving techniques, including differential privacy and secure multiparty computation. Differential privacy adds controlled noise to local computations, protecting sensitive data from inference attacks while contributing to model training. Secure multiparty computation enables collaborative computation without exposing raw data to other parties.

The system also includes a Data Residency Manager, which enforces location-based processing policies. For instance, data from the European Union is processed exclusively within EU-based cloud providers to meet GDPR requirements. The orchestration engine identifies compliant cloud providers and configures workflows accordingly.

During operation, this embodiment continuously audits data processing activities, generating compliance reports that document adherence to regulations. These reports are stored securely for future reference, ensuring transparency and accountability. By integrating privacy and compliance mechanisms, this embodiment makes the system particularly suited for sensitive industries such as healthcare, finance, and government.

The third embodiment leverages adaptive feedback loops to optimize the performance of federated learning workflows in real time. This embodiment introduces an Intelligent Feedback Module that collects real-time metrics on workload performance, resource utilization, and model convergence rates. These metrics are fed back into the orchestration engine, enabling it to make informed adjustments to resource allocation and workflow configurations.

The orchestration engine uses machine learning algorithms to predict resource demands based on historical patterns and current workload characteristics. For instance, if the system detects a slowdown in model training due to network latency between cloud nodes, it dynamically reassigns tasks to nodes with lower latency. Similarly, if a model's accuracy is improving slower than expected, the system reallocates additional computational resources to the corresponding cloud node to accelerate convergence.

This embodiment also incorporates Cross-Cloud Data Caching, which reduces latency and improves model synchronization efficiency. By caching intermediate results across multiple clouds, the system minimizes redundant computations and accelerates workflow progress.

The adaptive feedback mechanism ensures that the system remains resilient to unexpected changes in cloud environments, such as resource outages or performance degradation. This makes the embodiment ideal for high-stakes applications, such as real-time analytics or autonomous systems, where performance and reliability are critical.

While considerable emphasis has been placed herein on the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the invention. These and other changes in the preferred embodiments of the invention will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter to be implemented merely as illustrative of the invention and not as limitation.
, Claims:1. A system for adaptive multi-cloud orchestration of machine learning workflows, comprising:
a cloud management layer configured to interface with multiple cloud service providers to retrieve resource availability and performance metrics;
a federated learning module configured to execute distributed model training on data subsets across different cloud environments;
an orchestration engine configured to dynamically allocate cloud resources based on workload demands, cost optimization criteria, and data privacy requirements;
a compliance and security layer that enforces regional data protection regulations during workflow orchestration.

2. A method for adaptive orchestration of machine learning workflows in a multi-cloud environment, the method comprising:
retrieving data on resource availability, performance, and cost from a plurality of cloud providers;
executing federated learning operations across cloud environments to train a machine learning model on distributed data subsets without data centralization;
dynamically reallocating cloud resources in response to workload demand, cost changes, and real-time performance feedback;
ensuring data privacy and compliance by enforcing location-based data processing policies.

3. The system of claim 1, wherein the cloud management layer further comprises a cost analysis module configured to select cloud resources based on real-time pricing fluctuations across cloud providers.

4. The system of claim 1, wherein the orchestration engine includes an adaptive algorithm that reconfigures cloud resources based on workload patterns and historical performance data.

5. The method of claim 2, wherein the compliance and security layer enforces data localization requirements by designating specific cloud providers based on data residency regulations.

6. The method of claim 2, further comprising monitoring and logging the federated learning workflow across multiple clouds to track model accuracy, resource usage, and regulatory compliance.

7. The system of claim 1, wherein the federated learning module further incorporates privacy-preserving techniques, including differential privacy or secure multi-party computation, to enhance data security.

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