Federated ML Workflow Orchestration via Blockchain Agents
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Solution Overview
Problem
Current machine learning (ML) workflow orchestration technologies are centrally controlled, leading to architectural bottlenecks, scaling issues, and single points of failure, particularly in edge-computing scenarios and multi-organizational environments with heterogeneous resources, where data silos and resource heterogeneity hinder seamless collaboration across silos.
Innovation Solution
A decentralized federated ML workflow solution using a blockchain-based framework that enables orchestration across multiple resource domains, employing agents to manage workflow states and abstract resource access, thereby eliminating the need for a centralized controller and allowing for heterogeneous environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If centralized ML workflow orchestration is used, then workflow control and coordination are simplified, but architectural bottlenecks and single points of failure occur
Solution Approach 1:
The centralized orchestrator is segmented into multiple distributed agents deployed across different resource domains. Each agent manages workflow execution locally while maintaining consistency through blockchain-based state management, eliminating single points of failure while preserving coordination capabilities.
Solution Approach 2:
A blockchain-based intermediary layer is introduced between workflow tasks and resource domains. This intermediary manages state transitions and coordination without requiring a centralized controller, providing both distributed reliability and automated workflow control through smart contracts.
2Ease of operation
If centralized orchestration is used, then workflow management is simplified, but scaling issues arise in edge-computing scenarios
Solution Approach 1:
The orchestration functionality is segmented and distributed to edge agents that can independently manage workflows locally. This enables parallel workflow execution across multiple edge devices, achieving linear scaling while maintaining simplified management through standardized blockchain interfaces.
Solution Approach 2:
The system transitions from a single-dimensional centralized control model to a multi-dimensional distributed architecture where workflow management occurs simultaneously at local agent level and global blockchain level, enabling scaling across hierarchical dimensions from edge to cloud.
3Ease of operation
If centralized controller is used, then coordination is straightforward, but data silos and resource heterogeneity hinder collaboration
Solution Approach 1:
The blockchain-based task state management system provides a universal interface that works across heterogeneous resource domains and organizational silos. Standardized smart contracts enable coordinated workflow execution across diverse environments without requiring domain-specific integration logic.
Solution Approach 2:
The blockchain intermediary translates between different resource domain interfaces and workflow coordination requirements, enabling collaboration across silos and heterogeneous systems while maintaining straightforward coordination through automated smart contract execution.
4Reliability
If decentralized federated workflow solution is used, then fault tolerance and scalability are improved, but system complexity increases
Solution Approach 1:
The decentralized agents autonomously manage their own state and execution, self-organizing through blockchain-based consensus mechanisms. This self-service capability provides fault tolerance through distributed redundancy while keeping individual agent complexity low through standardized interfaces.
Solution Approach 2:
The system uses replicated state machines where each agent maintains a copy of the workflow state on the blockchain. This copying mechanism provides fault tolerance through redundancy while keeping each node's complexity manageable through consistent state replication protocols.
Data Source
AI summary
Systems and methods provide for a federated workflow solution to orchestrate entire machine learning (ML) workflows comprising multiple tasks, across silos. In other words, one or more sets/pluralities of tasks making up an ML workflow, can be executed across multiple resource partitions or domains. Federated workflow state can be maintained and shared through some form of distributed database/ledger, such as a blockchain. Agents that are locally deployed locally at the silos may orchestrate an ML workflow at a particular resource domains, each such agent having access, via the blockchain (acting as a globally visible/consistent state store), to the aforementioned workflow state. Such systems are capable of operating regardless of the existence of heterogeneous resources/aspects of a silo.