Federated AI Inference Orchestration for Mobile Workload Partitioning

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Solution Overview

Problem

Existing systems face challenges in efficiently distributing AI workload across mobile devices with limited processing capabilities and varying network conditions, leading to inefficiencies and potential overloading, while maintaining data privacy and ensuring uninterrupted inference.

Innovation Solution

A method that adaptively partitions AI workloads across a federated inference infrastructure based on real-time assessments of device capabilities and network conditions, employing rule-based decisions and machine learning to optimize workload distribution, predict future demands, and implement fault-tolerance strategies.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If AI workloads are distributed across mobile devices, then processing efficiency is improved, but device overloading and network congestion occur

Engineering Contradiction:

Improveprocessing efficiencyVSAvoiddevice overload

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The patent implements dynamic workload orchestration that continuously monitors device capabilities, network conditions, and workload characteristics to adaptively adjust task distribution in real-time. The system transitions from static to dynamic allocation, allowing the federated inference infrastructure to respond to changing conditions and prevent device overloading while maintaining high processing efficiency.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system changes multiple parameters simultaneously including device computational capacity assessment, network bandwidth availability, latency thresholds, and workload partitioning strategies. By dynamically adjusting these parameters based on real-time conditions, the system optimizes the balance between processing efficiency and device reliability.

Inventive Principle:

Principle #35Parameter changes

2Loss of energy

If LLMs are deployed on mobile devices, then bandwidth requirements are reduced, but processing capabilities and memory constraints are exceeded

Engineering Contradiction:

Improvebandwidth consumptionVSAvoidprocessing capability

Core Design Contradiction:

Loss of energyVSDevice complexity

Solution Approach 1:

The patent segments the large language model into multiple smaller model partitions that can be distributed across multiple mobile devices. Each device processes a specific segment of the inference workload, allowing the system to handle LLM tasks without requiring any single device to have full LLM processing capabilities. This segmentation reduces individual device complexity requirements while maintaining overall system capability.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system merges the computational capabilities of multiple resource-constrained mobile devices to collectively perform LLM inference tasks. By combining resources across the federated network, the system achieves LLM-level processing power without requiring individual devices to have sufficient standalone capabilities, thus reducing bandwidth requirements while working within device constraints.

Inventive Principle:

Principle #5Merging (Combining)

3Productivity

If tasks are reassigned dynamically, then resource utilization is optimized, but system complexity increases

Engineering Contradiction:

Improveresource utilizationVSAvoidorchestration complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent introduces an intermediary orchestration layer that manages task assignment and reassignment across devices. This intermediary component handles the complexity of dynamic resource allocation, monitoring device states and making intelligent decisions about task migration. By centralizing the orchestration logic in a dedicated intermediary system, the complexity is managed separately from individual devices while still achieving optimized resource utilization.

Inventive Principle:

Principle #24Intermediary (Mediator)

4Reliability

If fault-tolerance mechanisms are implemented, then system reliability is improved, but processing overhead increases

Engineering Contradiction:

Improvefault toleranceVSAvoidprocessing overhead

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system performs preliminary assessments of device capabilities and workload characteristics before assigning tasks. By evaluating device suitability in advance and preparing contingency plans, the system can quickly respond to failures without extensive overhead during normal operation. The preliminary action includes monitoring device health metrics and pre-identifying alternative devices for failover scenarios.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent implements feedback mechanisms that continuously monitor device performance, task completion status, and error conditions. This real-time feedback enables the system to detect failures early and trigger appropriate fault-tolerance responses. The feedback loop allows the system to maintain reliability while minimizing overhead by only activating fault-tolerance mechanisms when actually needed rather than continuously.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20260072730A1Optimized orchestration in federated inference across mobile devices

Publication Date: 2026.03.12 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

Rule-based decision is augmented by adaptively partitioning artificial intelligence (AI) workloads across a federated inference infrastructure based on real-time assessments of device capabilities, network conditions, and workload requirements. Inference demands are partitioned to distribute tasks, while supporting mobile device heterogeneity.