Introduction: From "Idle Computer" to "AI Training Artifact"

Imagine your home gaming rig, your office's underutilised servers, or even that dust-gathering NAS device becoming computational nodes capable of training ChatGPT-level large models. This isn't science fiction—it's an unfolding technological revolution.

Much like Uber transformed idle cars into shared transport tools, edge computing is now converting hundreds of millions of idle devices worldwide into a distributed AI training network. Today, we'll demystify how this ‘computing power sharing economy’ operates in accessible terms.

==============================================================
Core questions answered: Three key questions

Question 1: How is computing power split implemented?

Living metaphor: breaking down a big house into smaller rooms

Imagine you're renovating a large villa, but each worker can only handle one small room. You need to break down the entire renovation task into:

• The plumber is responsible for the pipes and circuits

• The mason is responsible for the walls and floors

• The carpenter is responsible for doors, Windows and furniture

• The painter is responsible for painting and decorating

The same goes for computing power splitting in edge computing:

Entry-level explanation: Take a large AI model (say, 100 billion parameters) and break it into many small pieces. Each device is only

responsible for training a small part of the model, like a jigsaw puzzle, and then put all the pieces together to form the complete model.

Technological advancement:

Professional technical details:

1 .ZeRO Style Parameter Sharding Mechanism:

• Shard the model parameters into different GPUs by dimension

• Each GPU stores only 1lN of parameters, and the required parameters are loaded dynamically

• Parameter sharing is implemented through the parameter server mode

2. Split Learning Model Split:

• According to the network layer split model, the first half is on the client and the second half is on the server

• Protect data privacy while implementing distributed training

• Information is passed through the middle layer to avoid leakage of raw data

3. Federal Data Sharding:

• Each node is trained with local data and only gradient updates are uploaded

• Privacy is protected by secure aggregation algorithms

• Supporting asynchronous updates and fault tolerance

Problem 2: How to achieve distributed computing power?

Beginner's explanation:

• Task release: like issuing a taxi demand

• Resource matching: The system finds the most appropriate device

• Task execution: The device starts "accepting orders" training

• Results collection: Summary of training results

Upward class design:

Details of professional technical implementation: 1 .Intelligent Task Scheduling Algorithm:

• Based on the device capability scoring system (GPU model, video memory, network bandwidth, latency, reputation score)

• Support dynamic load balancing and task migration

• Implement priority queues and resource reservation mechanisms

2. Communication protocol optimization:

• Web RTC DataChannels: Solves NAT traversal problem and supports browser participation

• gRPC over TLS: efficient inter-service communication with support for streaming

• Asynchronous aggregation: reduces network wait time and improves overall efficiency

3. Resource management mechanism:

• Real-time monitoring of equipment status and performance indicators

• Adjust task allocation strategy dynamically

• Intelligent load balancing and failover

Question 3: What if the GPU drops midway? Will the data be lost?

Can the task continue? A life analogy: The backup doctor in surgery

Just as hospitals have backup doctors during surgery, distributed training has multiple safeguards:

Beginner's explanation:

• Checkpoint save: Save your progress regularly, just like a game save

• Multiple backup copies: Important tasks are handled simultaneously across multiple devices.

• Automatic recovery: Tasks continue automatically after the device comes back online.

Inclusive error tolerance mechanism:

Details of professional technical implementation:

1 Design of checkpoint mechanism:

• Incremental checkpoints: only save the changed parts, reducing storage overhead

• Distributed checkpoints: Split the checkpoints into multiple nodes

• Encrypted storage: Ensure the security of checkpoint data

• Versioning: Support for multiple version rollback and recovery

2. Redundant execution strategy:

• Multi-replica critical tasks: Important tasks are performed in parallel on 3-5 nodes

• Voting mechanism: Verify the correctness of results by majority vote

• Malicious node detection: identification and isolation of abnormal behavior nodes

• Dynamic adjustment: Adjust the number of copies according to network conditions

3. Fault recovery mechanism:

• Automatic detection: real-time monitoring of node status and network connections

• Task migration: Seamlessly transfer tasks to other available nodes

• State recovery: Recovery of training status from the most recent checkpoint

• Data consistency: Ensure that the restored data state is correct

4. Data security:

• Encrypted transmission: All data is encrypted

• Distributed backup: Data is backed up and stored on multiple nodes

• Blockchain records: Key operations are recorded on the blockchain

• Access control: strict permission management and identity authentication

==============================================================

Technology enables deep analysis

Core algorithm: Make distributed training more efficient

1. Communication optimization: Reduce the time to "wait for data"

Problem analysis: How to reduce communication overhead when the bandwidth of home network is limited?

Technical solutions:

Implementation details:

• Gradient compression: only transmit important gradient updates, reducing communication by 90%

• Asynchronous aggregation: aggregates completed updates without waiting for all nodes

• Local aggregation: Aggregation within nodes in the same region, then uploaded to the central hub.

2. Memory optimization: Let ordinary GPU also train large models

Problem analysis: How to train large models with insufficient video memory on a single card?

Technical solutions:

Implementation details:

• Parameter sharding: Distributing model parameters across multiple cards, with each card storing only 1/N.

• Activated computation: Trading time for space by recalculating activation values on demand.

• CPU offloading: Put some parameters in memory and load them when the GPU needs them.

3. Secure aggregation: Protect privacy while enabling collaboration

Problem analysis: How to collaborate in training without data leakage?

Technical solutions:

Implementation details:

• Differential privacy: adding noise to protect privacy and control the loss of accuracy

• Secure multi-party computation: encrypted aggregated gradients, mathematically ensuring privacy security.

• Federated learning: data stays local, only model parameters are shared.

==============================================================

Real-world application scenario: Let technology

truly serve life scenario 1: Home AI assistant training

User story: Sam wants to train an AI assistant that can understand his family dialect.

Technical implementation process:

Value embodiment:

• Privacy protection: Dialect data will not be uploaded to the cloud

• Cost reduction: No need to rent expensive cloud servers

• Personalization: The model is specially adapted to the language habits of Sam's family.

Scenario 2: Enterprise data security training

User story: A bank needs to train a risk control model, but the data cannot be exported from the bank's

Technical implementation process:

Value embodiment:

• Compliance: meet financial data security requirements

• Efficiency: Multiple servers train in parallel

• Traceability: The training process is fully auditable.

Scenario 3: Scientific research collaboration and innovation

User story: Collaborative research on new drug technology in many laboratories around the world.

Technical implementation process:

Value embodiment:

• Knowledge sharing: accelerating scientific progress

• Privacy: protection of trade secrets

• Cost allocation: reduce R&D costs

==============================================================

Technical challenges and solutions

Challenge 1: Network instability

Problem description: The home network is often disconnected, which affects the training progress

Solution architecture:

Technical detail ：

• Breakpoint continuation: Regularly save training status and support recovery from any point

• Task migration: automatically detect network status and seamlessly switch nodes

• Asynchronous training: Improves fault tolerance by not waiting for all nodes to synchronize

• Smart reconnect: automatically detect network recovery and rejoin the training challenge

2: device performance differences

Problem description: GPU performance varies greatly between different devices

Solution architecture:

Technical detail ：

• Intelligent scheduling: Assign tasks according to the capability score of the device

• Load balancing: dynamically adjust task allocation to avoid performance bottlenecks

• Heterogeneous training: adapt to different hardware configurations and make full use of resources

• Dynamic adjustment: real-time monitoring of performance, adjusting training strategies

Challenge 3: safety risks

Problem description: Malicious nodes may disrupt the training process

Solution architecture:

Technical detail ：

• Results verification: multi-node cross-validation, detection of abnormal results

• Credit system: record the historical performance of nodes and establish a trust mechanism

• Encryption communication: end-to-end encryption to protect data transmission security

• Access control: strict access control to prevent unauthorized access

==============================================================

Future outlook: A new era of computing power democratization

Technology development trends

2024-2026: Infrastructure improvements

2026-2028: Application scenarios explode

2028-2030: Ecological maturity

Social influence

Economic level:

• Create new employment opportunities

• Lower the threshold for AI application

• Promote the optimised allocation of computing resources

Societal level：

• Protecting personal privacy

• Promoting the democratisation of technology

• Narrowing the digital divide

Technical level：

• Accelerate the development of AI technology

• Promote the adoption of edge computing

• Foster cross-disciplinary collaboration

=============================================================

Conclusion: Let everyone participate in the AI revolution

The edge computing distributed computing network isn't just a technological upgrade—it's a social revolution reshaping the power dynamics of computing. Just as the internet empowered everyone to become content creators, edge computing is now enabling anyone to become an AI trainer.

For ordinary users: Your idle devices can create value and participate in the AI revolution For developers: Lower costs and more possibilities for innovation For enterprises: Protect data security and improve training efficiency For society: Democratization of computing power and universal access to technology

By combining technological idealism with engineering pragmatism, we are building a more open, fair, and efficient computing future where everyone can participate in and benefit from it.

==============================================================

" Technology should not be the privilege of a few, but a tool that everyone can understand and use. Edge computing makes AI training go from the cloud to the edge, from monopoly to democracy, from expensive to universal. "

--Bitroot Technical Team

Introduction: From "Idle Computer" to "AI Training Artifact"

Imagine your home gaming rig, your office's underutilised servers, or even that dust-gathering NAS device becoming computational nodes capable of training ChatGPT-level large models. This isn't science fiction—it's an unfolding technological revolution.

Much like Uber transformed idle cars into shared transport tools, edge computing is now converting hundreds of millions of idle devices worldwide into a distributed AI training network. Today, we'll demystify how this ‘computing power sharing economy’ operates in accessible terms.

==============================================================
Core questions answered: Three key questions

Question 1: How is computing power split implemented?

Living metaphor: breaking down a big house into smaller rooms

Imagine you're renovating a large villa, but each worker can only handle one small room. You need to break down the entire renovation task into:

• The plumber is responsible for the pipes and circuits

• The mason is responsible for the walls and floors

• The carpenter is responsible for doors, Windows and furniture

• The painter is responsible for painting and decorating

The same goes for computing power splitting in edge computing:

Entry-level explanation: Take a large AI model (say, 100 billion parameters) and break it into many small pieces. Each device is only

responsible for training a small part of the model, like a jigsaw puzzle, and then put all the pieces together to form the complete model.

Technological advancement:

Professional technical details:

1 .ZeRO Style Parameter Sharding Mechanism:

• Shard the model parameters into different GPUs by dimension

• Each GPU stores only 1lN of parameters, and the required parameters are loaded dynamically

• Parameter sharing is implemented through the parameter server mode

2. Split Learning Model Split:

• According to the network layer split model, the first half is on the client and the second half is on the server

• Protect data privacy while implementing distributed training

• Information is passed through the middle layer to avoid leakage of raw data

3. Federal Data Sharding:

• Each node is trained with local data and only gradient updates are uploaded

• Privacy is protected by secure aggregation algorithms

• Supporting asynchronous updates and fault tolerance

Problem 2: How to achieve distributed computing power?

Beginner's explanation:

• Task release: like issuing a taxi demand

• Resource matching: The system finds the most appropriate device

• Task execution: The device starts "accepting orders" training

• Results collection: Summary of training results

Upward class design:

Details of professional technical implementation: 1 .Intelligent Task Scheduling Algorithm:

• Based on the device capability scoring system (GPU model, video memory, network bandwidth, latency, reputation score)

• Support dynamic load balancing and task migration

• Implement priority queues and resource reservation mechanisms

2. Communication protocol optimization:

• Web RTC DataChannels: Solves NAT traversal problem and supports browser participation

• gRPC over TLS: efficient inter-service communication with support for streaming

• Asynchronous aggregation: reduces network wait time and improves overall efficiency

3. Resource management mechanism:

• Real-time monitoring of equipment status and performance indicators

• Adjust task allocation strategy dynamically

• Intelligent load balancing and failover

Question 3: What if the GPU drops midway? Will the data be lost?

Can the task continue? A life analogy: The backup doctor in surgery

Just as hospitals have backup doctors during surgery, distributed training has multiple safeguards:

Beginner's explanation:

• Checkpoint save: Save your progress regularly, just like a game save

• Multiple backup copies: Important tasks are handled simultaneously across multiple devices.

• Automatic recovery: Tasks continue automatically after the device comes back online.

Inclusive error tolerance mechanism:

Details of professional technical implementation:

1 Design of checkpoint mechanism:

• Incremental checkpoints: only save the changed parts, reducing storage overhead

• Distributed checkpoints: Split the checkpoints into multiple nodes

• Encrypted storage: Ensure the security of checkpoint data

• Versioning: Support for multiple version rollback and recovery

2. Redundant execution strategy:

• Multi-replica critical tasks: Important tasks are performed in parallel on 3-5 nodes

• Voting mechanism: Verify the correctness of results by majority vote

• Malicious node detection: identification and isolation of abnormal behavior nodes

• Dynamic adjustment: Adjust the number of copies according to network conditions

3. Fault recovery mechanism:

• Automatic detection: real-time monitoring of node status and network connections

• Task migration: Seamlessly transfer tasks to other available nodes

• State recovery: Recovery of training status from the most recent checkpoint

• Data consistency: Ensure that the restored data state is correct

4. Data security:

• Encrypted transmission: All data is encrypted

• Distributed backup: Data is backed up and stored on multiple nodes

• Blockchain records: Key operations are recorded on the blockchain

• Access control: strict permission management and identity authentication

==============================================================

Technology enables deep analysis

Core algorithm: Make distributed training more efficient

1. Communication optimization: Reduce the time to "wait for data"

Problem analysis: How to reduce communication overhead when the bandwidth of home network is limited?

Technical solutions:

Implementation details:

• Gradient compression: only transmit important gradient updates, reducing communication by 90%

• Asynchronous aggregation: aggregates completed updates without waiting for all nodes

• Local aggregation: Aggregation within nodes in the same region, then uploaded to the central hub.

2. Memory optimization: Let ordinary GPU also train large models

Problem analysis: How to train large models with insufficient video memory on a single card?

Technical solutions:

Implementation details:

• Parameter sharding: Distributing model parameters across multiple cards, with each card storing only 1/N.

• Activated computation: Trading time for space by recalculating activation values on demand.

• CPU offloading: Put some parameters in memory and load them when the GPU needs them.

3. Secure aggregation: Protect privacy while enabling collaboration

Problem analysis: How to collaborate in training without data leakage?

Technical solutions:

Implementation details:

• Differential privacy: adding noise to protect privacy and control the loss of accuracy

• Secure multi-party computation: encrypted aggregated gradients, mathematically ensuring privacy security.

• Federated learning: data stays local, only model parameters are shared.

==============================================================

Real-world application scenario: Let technology

truly serve life scenario 1: Home AI assistant training

User story: Sam wants to train an AI assistant that can understand his family dialect.

Technical implementation process:

Value embodiment:

• Privacy protection: Dialect data will not be uploaded to the cloud

• Cost reduction: No need to rent expensive cloud servers

• Personalization: The model is specially adapted to the language habits of Sam's family.

Scenario 2: Enterprise data security training

User story: A bank needs to train a risk control model, but the data cannot be exported from the bank's

Technical implementation process:

Value embodiment:

• Compliance: meet financial data security requirements

• Efficiency: Multiple servers train in parallel

• Traceability: The training process is fully auditable.

Scenario 3: Scientific research collaboration and innovation

User story: Collaborative research on new drug technology in many laboratories around the world.

Technical implementation process:

Value embodiment:

• Knowledge sharing: accelerating scientific progress

• Privacy: protection of trade secrets

• Cost allocation: reduce R&D costs

==============================================================

Technical challenges and solutions

Challenge 1: Network instability

Problem description: The home network is often disconnected, which affects the training progress

Solution architecture:

Technical detail ：

• Breakpoint continuation: Regularly save training status and support recovery from any point

• Task migration: automatically detect network status and seamlessly switch nodes

• Asynchronous training: Improves fault tolerance by not waiting for all nodes to synchronize

• Smart reconnect: automatically detect network recovery and rejoin the training challenge

2: device performance differences

Problem description: GPU performance varies greatly between different devices

Solution architecture:

Technical detail ：

• Intelligent scheduling: Assign tasks according to the capability score of the device

• Load balancing: dynamically adjust task allocation to avoid performance bottlenecks

• Heterogeneous training: adapt to different hardware configurations and make full use of resources

• Dynamic adjustment: real-time monitoring of performance, adjusting training strategies

Challenge 3: safety risks

Problem description: Malicious nodes may disrupt the training process

Solution architecture:

Technical detail ：

• Results verification: multi-node cross-validation, detection of abnormal results

• Credit system: record the historical performance of nodes and establish a trust mechanism

• Encryption communication: end-to-end encryption to protect data transmission security

• Access control: strict access control to prevent unauthorized access

==============================================================

Future outlook: A new era of computing power democratization

Technology development trends

2024-2026: Infrastructure improvements

2026-2028: Application scenarios explode

2028-2030: Ecological maturity

Social influence

Economic level:

• Create new employment opportunities

• Lower the threshold for AI application

• Promote the optimised allocation of computing resources

Societal level：

• Protecting personal privacy

• Promoting the democratisation of technology

• Narrowing the digital divide

Technical level：

• Accelerate the development of AI technology

• Promote the adoption of edge computing

• Foster cross-disciplinary collaboration

=============================================================

Conclusion: Let everyone participate in the AI revolution

The edge computing distributed computing network isn't just a technological upgrade—it's a social revolution reshaping the power dynamics of computing. Just as the internet empowered everyone to become content creators, edge computing is now enabling anyone to become an AI trainer.

For ordinary users: Your idle devices can create value and participate in the AI revolution For developers: Lower costs and more possibilities for innovation For enterprises: Protect data security and improve training efficiency For society: Democratization of computing power and universal access to technology

By combining technological idealism with engineering pragmatism, we are building a more open, fair, and efficient computing future where everyone can participate in and benefit from it.

==============================================================

" Technology should not be the privilege of a few, but a tool that everyone can understand and use. Edge computing makes AI training go from the cloud to the edge, from monopoly to democracy, from expensive to universal. "

--Bitroot Technical Team