Highlights:
- Federated learning is a technique for training AI models that do not involve exposing or accessing your data, thereby providing a means of unlocking information to fuel novel AI applications.
- Some potential applications of federated learning include improving mobile applications, healthcare, autonomous vehicles, and predictive manufacturing maintenance.
AI and ML algorithms are finding extensive application in various business domains, ranging from customer service chatbots to facilitating decision-making by making predictions.
However, effective implementation of these models often necessitates access to essential data for training.
This requirement can challenge organizations that handle sensitive customer or proprietary data. They may be reluctant to share such information with third parties or other departments within the same organization.
To tackle this issue, federated learning has emerged as a machine learning technique that enables organizations to train AI models using distributed data without the need for centralization or data sharing.
This approach enables businesses to leverage AI for enhancing decision-making while maintaining data privacy and avoiding the risk of violating personal information.
In this article, we will delve into the concept of federated learning, its mechanism, the various scenarios where it can be implemented, and its advantages and disadvantages.
What is Federated Learning?
Federated learning is a technique for training AI models that do not involve exposing or accessing your data, thereby providing a means of unlocking information to fuel novel AI applications.
The spam filters, chatbots, and recommendation tools that are ubiquitous in our daily lives owe their existence to data, which was either scraped from the internet or contributed by users in exchange for free services.
Many of these AI models were trained on data collected and processed in a centralized location.
However, the current trend in AI is towards a decentralized approach, where new AI models are collaboratively trained on the edge, using data that never leaves your mobile device, laptop, or private server.
This new approach to AI training is called federated learning. It is quickly becoming the standard for complying with a range of regulations governing sensitive data handling and storage.
Federated learning also allows organizations to tap into the wealth of raw data generated by sensors on satellites, machines, bridges, and a growing array of ubiquitous smart devices in our daily lives.
But with so much data allowed, how does it process information and discard something irrelevant and sensitive?
How Does Federated Learning Work?
In machine learning, there are two key steps: training and inference.
During the training phase of federated learning, local machine learning models are trained on diverse and heterogeneous datasets stored locally on individual devices.
For example, when users interact with a machine learning application, they may identify and correct inaccuracies in its predictions. These corrections build a local training dataset on the user’s device.
Periodically, the parameters of the local models are shared between the local data centers. In many models, these parameters are encrypted before being exchanged to ensure the privacy and security of the local data samples.
Local data samples are not shared between the data centers despite sharing model parameters.
The aggregated local models are then used to build a shared global model that reflects the collective knowledge of the individual local models. The attributes of the global model are shared with the local data centers, allowing them to incorporate the global model into their own machine-learning models at a local level.
The global model is stored on the user’s device during inference. This allows rapid predictions to be generated locally on the user’s device using the trained model.
By keeping the model on the user’s device, federated learning ensures that the data remains private and secure while still allowing for the creation of accurate predictions.
With a clearer understanding of federated learning, let’s explore the potential beneficiaries and industries that can harness the power of this innovative technology.
What Are Potential Use Cases And Examples Of Federated Learning?
Federated learning is a flexible technique that can be applied to different machine learning scenarios, but the data type and context are important factors to consider. Some potential applications of federated learning include improving mobile applications, healthcare, autonomous vehicles, and predictive manufacturing maintenance.
Mobile Applications: Federated learning can help build models of user behavior without compromising personal data. This approach can improve on-device machine learning models for various applications, such as voice recognition, face detection, and next-word prediction. Google already uses federated learning to enhance Google Assistant’s “Hey Google” feature for voice commands.
Healthcare: Hospitals handle vast volumes of patient data for predictive healthcare purposes, necessitating adherence to stringent privacy laws and practices. Any breach could lead to legal, administrative, or ethical complications. Federated learning emerges as a solution for applications that require localized data, alleviating network strain and facilitating secure learning across multiple devices and organizations.
Automobiles/vehicles: Federated learning can enable real-time data and predictions, which are critical for safe and efficient driving. With continual learning, models can improve with input from different vehicles, leading to safer and more reliable self-driving cars. A study has shown that federated learning can decrease the training time for predicting wheel steering angles in autonomous vehicles
Manufacturing: Manufacturers can utilize federated learning to create predictive maintenance models for their equipment. This approach can overcome barriers such as customer reluctance to share personal data or data exportation issues from different sites. Federated learning can use local datasets to handle these challenges and develop predictive maintenance models that help ensure better equipment uptime and reduce maintenance costs.
That federated learning can be helpful in many industries, but the real question is how it can benefit the industry.
What Are The Benefits Of Federated Learning?
Federated learning is a novel field in machine learning that offers several advantages over traditional centralized approaches. The key benefits of federated learning are as follows:
Enhanced data security: In federated learning, the training data is kept on individual devices, eliminating the need for a central data repository. This guarantees the security and confidentiality of sensitive data.
Increased data diversity: In some cases, merging data from various sources may not be feasible due to network unavailability or other challenges. Federated learning enables access to heterogeneous data, even when data sources can only communicate intermittently.
Real-time continual learning: Federated learning models are constantly updated using client data without the need to aggregate data for continual learning. This enables models to improve continuously in real-time.
Improved hardware efficiency: Unlike centralized machine learning models, federated learning models do not require a complex central server to process data. This reduces hardware complexity and improves the overall efficiency of the system.
Federated learning is an emerging field in machine learning that allows distributed devices to train machine learning models collaboratively without sharing data with a centralized server. While federated learning offers many advantages over traditional machine learning approaches, it also poses unique challenges.
As the old saying goes, everything has its ups and downs. We have covered the uses and benefits of federated learning; now, let us understand its limitations.
What Are The Challenges Of Federated Learning?
Understanding the challenges of federated learning is crucial for the successful implementation of federated learning in various applications, including mobile applications, healthcare, autonomous vehicles, and manufacturing. Here are the challenges of federated learning:
Investment requirements
System requirements for federated learning models include high bandwidth and storage capacity to facilitate frequent node communication.
Data privacy
In federated learning, data is not collected on a single entity/server but instead on multiple devices. This can increase the attack surface and potentially compromise data privacy. To address this issue, privacy-enhancing technologies like differential privacy, secure multiparty computation, and homomorphic encryption can enhance data privacy capabilities.
Performance limitations
The heterogeneity of data sources in federated learning can limit the generalization of the models from some devices, reducing the accuracy of the next model version. Additionally, there is a risk of indirect information leakage if a malicious member inserts hidden backdoors into the joint global model. Research is ongoing to improve the performance of federated learning.
Centralization
Although federated learning is decentralized, a degree of centralization is still involved in building new models. Blockchained federated learning (BlockFL) and other approaches have been proposed to construct zero-trust models of federated learning.
Conclusion
Federated learning revolutionizes how businesses approach machine learning, prompting organizations to explore this novel approach in-depth. By adopting federated learning, companies can scrutinize their current algorithms and enhance their artificial intelligence applications.
Nevertheless, a significant challenge that could impede the extensive adoption of this technology is the issue of trust. Researchers actively investigate various approaches and incentives to prevent parties from submitting fraudulent or dummy data. This ensures the integrity of the model and prevents individuals from exploiting its advantages without compromising their data privacy.
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