Introduction to federated learning

A decentralized approach to AI
Machine learning has become a cornerstone of modern digital systems, enabling applications to make data-driven decisions, automate tasks, and enhance user experiences.

Traditionally, machine learning follows a centralized model. This approach involves collecting data from various sources into a single, centralized repository, typically in a cloud environment or a dedicated data center.

Then, advanced algorithms are applied to this consolidated dataset, training predictive models that can be deployed to make decisions based on new incoming data.
However, the centralized machine learning paradigm is facing growing challenges. The explosion of connected devices, sensors, and distributed data sources has led to an exponential increase in the volume and complexity of data being generated.

At the same time, concerns around privacy, security, and regulatory compliance have made it increasingly difficult to freely move and consolidate data from different sources.

The data needed to train effective machine learning models is often distributed across organizations, devices, or clients, making centralization challenging due to privacy risks and high transfer costs.

Federated learning

Federated learning (FL) is a decentralized approach that tackles the issues of centralized machine learning by allowing models to be trained on data distributed across various locations without moving the data.
Instead, FL moves the computation to where the data exists, enabling the creation of strong, globally-informed models while preserving data privacy and security.

How federated learning works

In federated learning, AI models are trained across multiple devices or servers (called client nodes) without needing to move the data off those devices. Here’s a simplified breakdown of how it works:

1. Starting the global model
The process begins with a global model on a central server. This could be any type of machine learning model, like a neural network or decision tree.
2. Sending the model to clients
The server sends the global model’s parameters to a group of selected client nodes. Each client uses its own local dataset, which stays securely on the device.
3. Local training
Each client trains the model using its local data, adjusting the model’s parameters based on what it learns from the data. This training process is repeated for several rounds, rather than continuing until full accuracy is achieved.
4. Combining the updates
The updated models from each client are sent back to the central server, where they are combined. A common approach is called Federated Averaging, where the server takes a weighted average of the updates from each client.

At last, the improved global model is sent back to the clients for further training. This cycle continues until the model reaches a satisfactory level of accuracy.

Federated Learning Explained

Concepts, Benefits, and Practical Applications

This video explains the core concept of federated machine learning without getting into technical details.While federated learning is often recognized for its privacy-preserving capabilities, this video highlights additional benefits that could be transformative for various industries through a simple example.

Cross-silo and cross-device

Federated learning is often discussed in terms of the technology behind it, such as cross-silo and cross-device approaches or horizontal and vertical.

Cross-silo
Cross-silo federated learning typically involves collaboration between organizations (silos), each of which holds its own dataset. This can be seen in contexts like healthcare or finance, where different organizations train a shared model using their own data without exposing it to others.
Cross-device
Cross-device federated learning refers to training models across a vast number of devices, such as smartphones or IoT devices, where each device has a small local dataset. This scenario involves a large number of participants (devices), each contributing to the training process.
Horizontal
Horizontal federated learning is when different parties have datasets that share the same feature space, meaning the data has the same types of information or characteristics (e.g., different hospitals with similar patient data). This type is most common in both cross-silo and cross-device scenarios.
Vertical
Vertical federated learning is when different organizations have datasets with different features for the same samples. For example, a bank and an e-commerce platform might collaborate, where the bank has financial data, and the e-commerce platform has purchase history, both related to the same customers.

A data ownership perspective

Federated learning can also be understood from a business and collaboration perspective. It focuses on how it applies in different business contexts and helps deal with challenges related to data privacy, regulations, and managing different stakeholders.
Single model & data owner
The simplest form of federated learning involves a single company that owns both the machine learning model and the data. This setup simplifies implementation, especially when centralizing data isn’t possible due to technical challenges or privacy regulations. This approach is useful for companies that need to make use of their distributed data without centralizing it, especially when dealing with privacy or technical restrictions.
Industry collaborations
An interesting application of federated learning is industry-wide collaboration. Here, multiple companies, even competitors, work together to solve shared problems. Federated learning allows them to create joint models without sharing sensitive data. This approach is valuable when competitors need to work together on common challenges or when they want to develop a powerful model together to gain a competitive advantage.
Vendor-to-customer
Vendors train machine learning models on customer-owned data. Traditionally, this required customer consent and data centralization. Federated Learning (FL) offers a more privacy-friendly approach, allowing vendors to develop intelligent tools and services without directly accessing sensitive information. This is particularly useful when customers are reluctant to share data, enabling smarter services while maintaining data privacy.

Practical Applications of Federated Learning

Federated learning enables innovative solutions across various industries. Here are key application areas where this technology is making a significant impact:

Cross-Device AI Applications

Smartphones and Personal Devices

Federated learning is revolutionizing how AI services are delivered on personal devices while maintaining privacy:

  • Value Proposition: The advancement in AI services demands privacy-preserving mechanisms, especially for applications that operate across various devices like mobiles and customer service systems. These services include personalized recommendations, user behavior analysis, and enhanced user experience without compromising user privacy. The aim is to deliver sophisticated AI features while adhering to strict privacy standards.
  • Data Challenges: Cross-device AI applications typically require the aggregation and analysis of sensitive user data, such as personal preferences, behavior patterns, and communication content. This data is often subject to privacy regulations like GDPR, necessitating strict controls on data sharing and processing. The challenge is to leverage this data for AI without violating privacy norms or exposing sensitive information.
  • Federated Learning Solution: Federated Learning is ideal for this scenario as it allows for the decentralized training of AI models directly on users' devices. This approach ensures that sensitive data remains on the device, reducing the risks associated with data transfer and storage. It enables the collaborative improvement of AI models while maintaining data privacy and compliance with regulations, thus offering a robust solution for cross-device AI applications.

Data Collaborations for Industry Innovation

Air Traffic Management

The aviation industry demonstrates how federated learning can transform complex operational systems:

  • Value Proposition: The Air Traffic Management (ATM) industry is moving towards a digital European sky. Trajectory Based Operations (TBO) allows for the proper coordination of ATM constraints on traffic, before or during flight and the airspace users can fly the best trajectory possible safely and efficiently. The goal is to have more accurate trajectory predictions.
  • Data Challenges: TBO consist of seamless accurate prediction & optimisation of trajectories and ATM constrains through all the planning phases. Important relevant data spread out over several stakeholders in TBO is non-sharable data: too sensitive business data or protected by GDPR.
  • Federated Learning Solution: Federated Learning addresses these challenges by enabling privacy- preserving exploitation of private data (relevant for operations) for ML purposes, while stakeholders keeping the full ownership and control in their own data silo.

Healthcare and Medical AI

Medical Imaging and Diagnostics

The healthcare sector showcases federated learning's potential in handling sensitive medical data:

  • Value Proposition: There is a large potential in using AI in medicine. Use case include digital pathology, segmentation of organs and tumor, and detecting anomalies in time series data. Today doctors manually generate annotated training data. The software needs to learn from this user generated data to improve machine learning models. Without the data, they do not reach the required scale of data to train high performing models.
  • Data Challenges: The problem is that the data is sensitive, owned by the customers, and subject to health data regulations. Data cannot be be moved off site and shared, and cannot be used effectively to improve AI software. Single clinics struggle with obtaining diverse enough data in large enough quantities. Anonymization and pooling of data is both costly, time consuming, and risks reducing data utility.
  • Federated Learning Solution: With federated learning the regulatory challenge can be addressed and no data is moved off site. Without sharing the data, learnings from multiple clinics can be integrated to improve the machine learning models.

Sustainable Building Management

Energy-Smart Buildings

Federated learning is helping combat climate change through smart building management:

  • Value Proposition: Buildings, including homes, malls, factories, and hospitals, contribute to 37% of global carbon emissions, with energy consumption expected to rise by 70% by 2030 due to population growth and rising living standards. However, this can be mitigated by using AI-based smart systems connected to sensors in buildings, potentially reducing energy consumption and carbon emissions by up to 30% through optimized lighting, heating, and cooling.
  • Data Challenges: Smart buildings generate vast amounts of data from sensors and connected devices. Conventional techniques that centralize this data for analysis are not applicable in all cases due to various data transfer barriers such as regulations such as regional data transfer legislations, the data might be sensitive from a business perspective and/or owned by someone else, or, the volume of data might simply be so large that data transfer becomes a practical problem.
  • Federated Learning Solution: Federated learning addresses these challenges by enabling local processing of data and model training on edge hardware (e.g. gateway servers in the buildings). By learning from localized data, federated learning not only upholds user privacy but can also craft baseline models that effectively learn from different buildings or building zones. These generalizable models can then be further optimized for specific buildings and homes. Furthermore, this machine learning framework enables devices to collaboratively learn from each other, increasing the overall network's capabilities to optimize energy efficiency across multiple buildings. Last but not least, the concept of federated learning enables better management of combined geographical or property-specific models which can be adapted for new homes and buildings that lack prior training data.

Automotive and Transportation

Fleet Intelligence and Autonomous Driving

The transportation sector leverages federated learning for vehicle optimization:

Fleet Maintenance:

  • Value Proposition: Predictive maintenance of truck fleets requires federated on-vehicle training of models to benefit from collective maintenance experiences.
  • Data Challenges: Gathering data from entire truck fleets is costly and challenging due to regulatory constraints, connectivity issues, and the vast amount of data produced.
  • Federated Learning Solution: Federated Learning solves these issues by enabling on-vehicle machine learning model training, allowing each truck to learn from the maintenance data of the entire fleet without the need for data transfer.

Autonomous Vehicles:

  • Value Proposition: Autonomous driving systems need to enable federated on-device training of models in order to manage large scale, in-vehicle machine learning.
  • Data Challenges: Collecting data from all cars in use is expensive and in many cases impossible due to connection problems and the sheer quantity of data generated by modern cars.
  • Federated Learning Solution: Federated Learning addresses the challenges by training on-board machine learning models in a federated setting so that each single car can learn from individual, group and fleet data.

Edge Computing Applications

Edge AI Optimization

Federated learning plays a crucial role in edge computing scenarios, where computation needs to happen closer to where data is generated. This approach is becoming increasingly important with the proliferation of IoT devices and the need for real-time processing.

  • Value Proposition: Edge devices require immediate response capabilities for time-critical applications like industrial automation, autonomous systems, and smart security systems.
  • Data Challenges: Edge computing optimizes resource usage by processing data locally, reducing the burden on central servers and network infrastructure.
  • Federated Learning Solution: Local processing ensures continued operation even when network connectivity is limited or unavailable. By minimizing data transfer and cloud computing requirements, organizations can reduce operational costs.

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Challenges and considerations

Federated learning introduces a unique set of challenges that must be carefully managed to ensure the effectiveness and security of the learning process across distributed environments.

Complexity and coordination
Federated learning introduces added complexity compared to traditional machine learning. It involves training models across multiple devices or servers, each with its own data. This requires careful coordination of machine learning operations (MLOps) to ensure efficient distribution and aggregation of model updates, reliable communication between nodes, and robust security throughout the process.
Heterogeneity of systems and data
In FL, client devices can vary significantly in terms of hardware capabilities, software environments, and data quality. Additionally, the data on these devices is often non-IID (not independent and identically distributed), meaning it may not represent the overall population. This requires careful algorithm design, thoughtful aggregation of updates, and strategies to manage the variability in both systems and data.
Scalability
Federated learning systems must scale efficiently as the number of participating devices increases. Managing thousands or millions of devices simultaneously introduces challenges in coordinating updates, aggregating models, and handling device failures or dropouts.
Privacy and data leakage
Although federated learning enhances privacy by keeping data on client devices, there is still a risk of data leakage through model updates. Adversaries could attempt to infer private information from the gradients or updates shared during the training process. Safeguarding against such risks is crucial.

Federated Learning FAQ

Basics of Federated Learning

What is federated learning and how does it differ from traditional machine learning?

Federated learning is a machine learning approach where the model training process is distributed across multiple devices or servers, allowing data to remain on local devices. Unlike traditional machine learning, which requires centralizing data on a single server, federated learning aggregates model updates from local devices without transferring raw data. This approach enhances privacy and reduces data transfer costs.

How does federated learning ensure data privacy and security?

Federated learning ensures data privacy by keeping the data on local devices and only sharing model updates, which are aggregated to improve the global model. Techniques like differential privacy and secure multi-party computation can be used to further enhance privacy by adding noise to the updates or encrypting them during transmission.

What are the main types of federated learning architectures?

There are several main types:

  • Cross-silo: Involves collaboration between organizations (silos), typically used in healthcare or finance
  • Cross-device: Focuses on training across numerous devices like smartphones or IoT devices
  • Horizontal: Used when parties have datasets with the same feature space
  • Vertical: Applied when organizations have different features for the same samples

Implementation and Technical Aspects

How does the federated learning process work?

The process follows several key steps:

  1. A global model is initialized on a central server
  2. The model is sent to selected client nodes
  3. Clients train the model using local data
  4. Updated models are sent back to the server
  5. Updates are combined using techniques like Federated Averaging
  6. The improved global model is redistributed to clients

What are the common challenges in implementing federated learning?

Key challenges include:

  • Complexity and Coordination: Managing training across multiple devices/servers
  • System Heterogeneity: Dealing with varying hardware capabilities and environments
  • Data Heterogeneity: Handling non-IID data across different clients
  • Scalability: Managing large numbers of participating devices
  • Privacy Concerns: Preventing data leakage through model updates

Business and Organizational Aspects

What are the different ownership models in federated learning?

There are three main approaches:

  1. Single Model & Data Owner: One company owns both model and distributed data
  2. Industry Collaborations: Multiple companies work together on shared problems
  3. Vendor-to-Customer: Vendors train models on customer-owned data without direct access

How can organizations benefit from federated learning collaborations?

Organizations can:

  • Share learning benefits without sharing sensitive data
  • Comply with data privacy regulations while improving AI capabilities
  • Reduce costs associated with data transfer and centralization
  • Maintain competitive advantage while collaborating on common challenges

Applications and Use Cases

How is federated learning used in healthcare?

Healthcare applications include:

  • Digital pathology analysis
  • Organ and tumor segmentation
  • Anomaly detection in time series data
  • Training models across multiple clinics without sharing patient data
  • Improving AI software while maintaining regulatory compliance

What role does federated learning play in smart buildings?

Federated learning enables:

  • Optimization of energy consumption through local data processing
  • Creation of baseline models that learn from different buildings
  • Management of geographical or property-specific models
  • Reduction of carbon emissions through optimized building management
  • Privacy-preserving analysis of sensor data

How is federated learning applied in transportation?

Transportation applications include:

  • Fleet Management:
    • Predictive maintenance across truck fleets
    • Learning from collective maintenance experiences
    • On-vehicle model training without data transfer
  • Autonomous Driving:
    • In-vehicle machine learning at scale
    • Learning from individual and fleet-wide experiences
    • Managing large-scale data generation efficiently

What are the edge computing applications of federated learning?

Edge computing applications include:

  • Industrial IoT:
    • Smart manufacturing systems
    • Predictive maintenance
    • Quality control optimization
  • Smart Infrastructure:
    • Traffic management
    • Environmental monitoring
    • Smart grid optimization
  • Consumer Devices:
    • Smart home automation
    • Wearable devices
    • Mobile applications

How does federated learning benefit the aviation industry?

In aviation, federated learning:

  • Enables accurate trajectory predictions
  • Facilitates coordination of air traffic management
  • Allows stakeholders to maintain control of sensitive business data
  • Supports the transition to a digital European sky
  • Enables privacy-preserving exploitation of operational data

Future and Trends

What are the emerging trends in federated learning?

Emerging trends include:

  • Integration with edge computing for reduced latency
  • Enhanced privacy-preserving techniques
  • Improved model aggregation methods
  • Greater scalability for large-scale deployments
  • Cross-industry standardization efforts

What are the future challenges that need to be addressed?

Future challenges include:

  • Developing more efficient communication protocols
  • Improving model performance with non-IID data
  • Enhancing security against various types of attacks
  • Standardizing federated learning practices
  • Balancing privacy requirements with model performance

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