Distributed learning is a method of training and deploying machine learning models across multiple computing devices or nodes, such as mobile devices, edge servers, and cloud platforms, instead of relying on a single centralized system. This approach allows large-scale models to be trained faster, using the combined processing power and data from various locations.
Over the last decade, machine learning (ML) has undergone a significant transformation, shifting away from the traditional “big data” paradigm, where large datasets are aggregated and processed in centralized cloud environments, toward a “small data” paradigm.
In this new approach, distributed devices or agents handle their own data locally, often at the network edge. This shift renders conventional centralized ML methods, which depend on large, centralized datasets for effective inference, less practical. Instead, there is a growing demand for new distributed learning techniques that enable collaborative training and inference without requiring the exchange of raw data.
Distributed learning is a method of training and deploying machine learning models across multiple computing devices or nodes, such as mobile devices, edge servers, and cloud platforms, instead of relying on a single centralized system. This approach allows large-scale models to be trained faster, using the combined processing power and data from various locations.
These solutions must be designed with the inherently decentralized and multi-agent nature of modern systems in mind.
A practical illustration of this paradigm can be seen in the Internet of Things (IoT) and autonomous systems, such as Internet of Vehicles (IoV) or connected drones. In these settings, each device gathers its own private, often limited, dataset. To build effective models collectively, these devices must cooperate while avoiding direct data sharing, either due to privacy concerns or the limitations in communication and computational resources.
Distributed learning consists of two main phases:
Compared to centralized AI systems, distributed AI offers key advantages:
As a result, distributed AI is particularly well-suited for IoT and mobile internet scenarios.
Within the EECC (End-Edge-Cloud Computing) framework, a large deep neural network (DNN) can be segmented and executed across mobile devices, edge servers, and central cloud servers.
However, training deep learning models in this distributed setup often requires frequent exchange of gradients between nodes, which can lead to significant network overhead. To mitigate this, gradient compression techniques are applied to reduce communication costs.
Distributed learning involves partitioning large-scale machine learning tasks across multiple computational nodes, enabling parallel processing and efficient utilization of resources. While this approach enhances computational efficiency, it often necessitates the central aggregation of data, posing challenges related to data privacy and compliance with regulations.
With the rapid growth of billions of Internet of Things (IoT) devices, smartphones, and global digital platforms, recent years have seen an explosion of data generated across various endpoints; whether on individual mobile devices or within the data centers of different organizations.
Much of this data includes highly sensitive personal or institutional information, such as facial images, health records, geolocation data, or financial status. Transferring such raw data to centralized servers raises significant risks of data exposure and privacy violations.
In response to these concerns, numerous data protection laws have been enacted worldwide, including China’s Cybersecurity Law, the European Union’s General Data Protection Regulation (GDPR), Singapore’s Personal Data Protection Act (PDP), and California’s Consumer Privacy Act (CCPA), and Consumer Privacy Bill of Rights (CPBR) in the United States.
These legal frameworks significantly restrict the collection and centralization of data from multiple devices, regions, or institutions. Furthermore, computing and storage capabilities are often distributed geographically and organizationally, making it impractical to consolidate them within a single data center.
Introduced back in 2017, Federated Learning (FL) has emerged as a powerful and privacy-conscious method for leveraging distributed computing resources to collaboratively train machine learning models.
Unlike traditional centralized approaches that require raw data to be collected and stored in a central server or data center, FL enables multiple users or organizations to train a shared model while keeping data decentralized and stored locally. Instead of moving raw data, only model updates or processed information are exchanged between participants, preserving data confidentiality and meeting privacy regulations.
These distributed computing resources typically include users’ mobile devices or servers from various organizations. FL adopts a “bring the code to the data” strategy, effectively addressing key concerns around data privacy, ownership, and locality. This makes it especially valuable in contexts where legal or ethical restrictions prevent data aggregation.
In contrast, conventional centralized machine learning gathers data from diverse sources into one location, raising serious concerns regarding privacy, data breaches, and unauthorized access.
Centralized methods also face challenges related to scalability, computational demand, and secure data handling.
Federated Learning sets itself apart in three main ways:
Federated Learning can be classified in different ways.
Federated Learning (FL) can be classified into three main types depending on how data is partitioned across the sample and feature spaces.
Horizontal Federated Learning (HFL) applies to scenarios where all participants have data with the same feature types but different user samples.
Here, each participant trains a model locally and sends model updates, such as parameters or gradients, to a central server. The server then aggregates these updates and returns the combined results to all participants. The outcome of this process is a shared global model that each party can independently use during inference.
Vertical Federated Learning (VFL) refers to cases where the involved datasets contain information on the same users but consist of different types of features.
Here, both the data and models remain local to each participant. Instead of sharing model parameters, participants exchange intermediate computation results throughout the training process. After training, each participant retains its own local model. However, during inference, VFL requires collaboration between parties to generate predictions, as the complete information is distributed across participants.
Federated Transfer Learning (FTL) is used when datasets differ in both features and samples, with only a small portion of data overlapping between participants.
Here, like VFL, participants keep the local data and model and only exchange the intermediate results. Each party keeps a local model but unlike VFL, there is no need for collaboration during the inference stage.
Federated Learning (FL) can also be divided into two categories based on the type of participants:
Horizontal Federated Learning (HFL) can operate in both cross-device and cross-silo environments, while Vertical Federated Learning (VFL) is generally applied within cross-silo scenarios. Federated Transfer Learning (FTL) is also mostly applied to cross-silo scenarios.
With the collaborative efforts of researchers worldwide, Federated Learning (FL) has become increasingly influential across multiple industries.
In the healthcare sector, patient privacy is paramount, and regulations prevent hospitals from sharing sensitive medical data directly.
FL offers an ideal solution by allowing each hospital to train models locally on their own data, treating each institution as a client, while a central government agency acts as the server.
Hospitals download a shared initial model, train it with their internal data, and then upload encrypted model updates. These updates are aggregated to form a global model that achieves high performance in disease detection, without exposing any private data.
In recommendation systems, traditional methods require collecting and processing large volumes of user data, often raising privacy concerns and facing resistance due to data silos.
FL provides a privacy-aware alternative, particularly in cross-domain recommendation systems like short video apps, social media, and e-commerce platforms. Here, FL enables the use of collaborative filtering, deep learning, or meta-learning techniques in a federated manner.
In Vertical Federated Learning (VFL), the server can even incorporate user data from multiple domains, leading to more personalized and accurate content suggestions.
In smart city development, collaboration between government bodies, businesses, and citizens is essential. However, due to rising privacy regulations, central data centers can no longer freely share data with third parties.
FL helps overcome this barrier by connecting isolated data silos and allowing multiple urban institutions to collaboratively train models. Government departments and private organizations act as clients, while the city’s data management center serves as the coordinating server.
This enables better planning and services in areas like public transport, utilities, and citizen services, all while preserving data confidentiality.
In the finance and insurance industries, collaboration among institutions is becoming critical for leveraging big data. Many financial firms have started applying FL to areas such as risk management, marketing, and anti-money laundering.
Horizontal FL (HFL) enables banks to jointly train depositor credit prediction models, while Vertical FL (VFL) helps institutions predict borrowers’ repayment capacities using different types of data.
In both cases, financial institutions maintain ownership of their proprietary data while training secure and effective models to assess creditworthiness and customer investment potential.
As mobile and edge devices become increasingly intelligent, they offer a powerful foundation for secure, decentralized data processing.
Integrating edge computing with FL allows edge devices to collaboratively train high-performing models without transferring raw data to centralized cloud infrastructure.
This combination supports real-time, privacy-preserving AI applications across industries, empowering smarter systems at the edge of the network.
Intrusion detection is a widely used method in network security to identify and prevent unauthorized access. In recent years, deep learning techniques have become popular for enhancing intrusion detection systems due to their high accuracy in identifying threats.
However, training deep learning models typically requires large, diverse datasets, which many institutions lack, making it difficult to build effective models. Additionally, consolidating data from multiple institutions into a central server for training poses significant privacy risks.
Federated Learning (FL) offers a promising solution to these challenges by allowing institutions to collaboratively train models without sharing their sensitive data, thus maintaining privacy while improving detection performance.
This article explored the transition from distributed machine learning to federated learning, emphasizing its role in enabling collaborative model training while preserving data privacy. The information presented draws upon research from various academic sources.
The discussion highlighted how FL addresses key challenges across domains such as healthcare, finance, smart cities, recommendation systems, and network security. In particular, FL provides a secure framework for privacy-sensitive applications like intrusion detection, where centralized data aggregation is impractical.
By leveraging decentralized data and computation, federated learning not only enhances model performance but also ensures compliance with data protection regulations. As research and real-world deployment continue to advance, FL is expected to play a critical role in building scalable, trustworthy, and intelligent systems across industries.
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