Traditional centralized machine learning approaches have limitations with the huge volume of data getting generated at different locations rapidly at the same time. To address this challenge, decentralized approaches are in demand. Swarm learning, a nature-inspired computing technique, is being preferred by solution architects to implement decentralized machine learning systems. In this article, first, we will understand the issues with centralized learning techniques, then we will discuss the benefits of decentralized learning approaches. After that, we will understand how swarm learning can be an effective decentralized machine learning framework that can address the challenges of centralized learning systems. The major points to be covered in the article are listed below.
Table of Contents
- Problem with Centralized/Traditional Learning Techniques
- Expected Solutions with Decentralized Learning Techniques
- Swarm Learning
- Ant Colony Optimization
- Particle Swarm Optimization
- Application of Swarm Learning
Let us begin our discussion by understanding the problems faced by centralized learning techniques.
Problem with Centralized/Traditional Learning Techniques
There are various areas where we see an increasingly distributed nature of the data because data is increasing rapidly from various sources and this is a challenge for the centralized learning approaches. Let’s take an example of the self-driving car in which sensors such as LIDAR, radar, and vision are generating petabytes of data per day. Such data is generated at an unprecedented speed. and also the locations of the data generated are distributed because there are various sensors in the car. If this kind of data is stored in a centralized location it is formidable.
The second challenge to the traditional learning technique where the data is centralized in one location is the privacy and security of the data. Most of the centralized machine learning algorithms require consolidation data which next requires to move to different sources which often leads to leakage of the data and many of the industries like health where the data aggregates the people personal information, behavioural habits in it and dispersion in many sources can invade the person’s privacy.
Another limitation of the traditional centralized ML models comes from the data custody domain. We see that most of the companies are the generators of their own data and they use another entity to analyze, clean, and collect that data. These entities have their own infrastructure for performing the above-given action on the data. This separation of the data owner to the data custody entities makes data monopolies where large amounts of data profit go into the different entities ’ pockets.
Expected Solutions with Decentralized Learning Technique
As we just discussed the drawbacks and problems of the centralized learning technique. Many of them can be overcome by decentralized learning techniques. To understand it, let’s understand the following attributes of any decentralized machine learning approach:
As we know the ML whatever we are using with any kind of data should be accurate, efficient, and also capable of handling the high distribution of the data. Being more focused on the distribution of the data a decentralized learning algorithm needs to be more good at handling highly distributed data. Using their techniques of allocating workloads, connecting peers we should get higher results in terms of accuracy and efficiency. In addition, these learning techniques should have options for dealing with imbalanced data.
As we have talked about the security concern regarding the centralized learning techniques. The decentralized ML has the security features which should ensure that only trusted participants can be therein in the procedure of learning. These can be considered as the blockchain technology where only trusted participants can perform changes in the present and it has already been proven that blockchain has high security in comparison to other technology
One of the best features of decentralization is that they provide better privacy. Decentralization of ML should give better control to the owner over their sensitive information presented in the data.
In the above part, we have seen what are the drawbacks of the centralized learning techniques and how we can overcome them using the decentralized learning technique. There can be various decentralized learning methods and swarm learning is one of them using which we harness the power of distributed data. This type of learning is actually inspired through some natural or biological process. In the next section of the article, we are going to talk about Swarm Learning.
Swarm learning is a part of the artificial intelligence and machine learning studies where the major focus of swarm learning is to evaluate the behaviour of the decentralized system as we have seen in the above part of the article we have various problems with the centralized learning technique on which our most of the traditional models are dependent. A decentralized system may be helpful for us in overcoming the difficulties of centralized learning methods. The basic idea behind this learning is taken from the operational way of the ant colonies or bird flocks. Translate into computationally intelligent systems.
Taking the example of the bird flocks where to reach the destination, each bird takes the part and the action by the bird can be considered as its contribution in the global action of the reaching to the destination. In swarm learning, a global system is divided into agents with their environment. And interaction behaviour of the agents leads to the global solution behaviour. In terms of neural networks, we can say that these intersecting agents are the group of the neuron where they are basically working as a team on finding the best place to be.
We can say the swarm learning spreads through two major optimization techniques.
- Ant Colony Optimization(ACO)
- Particle Swarm Optimization(PSO)
Let us understand these techniques in detail.
Ant Colony Optimization
As we know how the ant colony works on finding food. We often see that ants go in a line or multiple lines towards food. Similarly, the Ant Colony Optimization (ACO) is a technique of solving computational problems or we can say that ACO is a probabilistic technique for finding good paths through graphs so that this can lead to the optimal solution of the problem. And the graph is a structure of a different related set of objects. A combination of artificial aunts and some algorithms which are heuristic methods for solving optimization problems can be considered as the ACO method for optimization of the graphs and can be used in vehicle routing or in internet routing.
The above image is a representation of choosing the shortest way between the two points A and B where the graphs are used to make the shortest length way. It is a hazard to define an Ant colony method. According to the different authors, the definition of ACO is different. We can roughly say that the Ant Colony optimization technique is a populated metaheuristic with each solution represented by an ant moving in the search space. The Below image is a representation of the ant colony finding the shortest way to food from the initial stage to the final stage.
Particle Swarm Optimization
As we have seen in the Ant Colony optimization we were using the graph for finding an optimal solution of the problem .here this is also an optimization technique that is basically dependent on the methods where we try to improve the candidate solution in an iterative manner to find an optimal solution of any problem for the set of the solutions. The set of candidate solutions can be considered as the particles and moving these particles in the search space gives the solution of the problem where simple mathematical functions are used regarding the position and the velocity of the particle in the movement. The movement of any particle is always influenced by its best position in the search space and by moving the functions that guide the particle to that space. By these procedures, it is expected to move the swarm toward the best solution.
The above image is a representation of the PSO where particles are searching for the global minimum of a function.
This optimization technique is also a metaheuristic because it also doesn’t make any assumptions about the problem and also using this we can search very large spaces.
In the above, we have seen what swarm learning is and how we can approach the different optimization techniques coming under swarm learning. There can be various applications of swarm learning, some of the important applications of swarm learning we are going to discuss in the next section of the article.
Application of Swarm Learning
By the above intuition, we can say that swarm learning is a way to perform decentralized learning where it can be treated as the remedy for us where we were facing the challenges of centralized learning. Also, there are a lot of benefits of swarm learning. It can be used in a wide range of varieties of the domain. And some of the important domains where it played an important role are listed below:
- Swarm learning can be used in controlling road traffic. One of the practicable examples is the U.S. military where they are using swarm techniques for controlling unmanned vehicles.
- Also in space technology, there are uses of swarm learning. The European Space Agency and NASA are using swarm technology for solving problems regarding space technologies where for planetary mapping the NASA is using swarm learning and is developing orbital swarms for self-assembly and interferometry.
- There are some examples of the usage of swarm learning in the medical domain like in 1992 George A. Bekey discusses the possibility of using swarm learning to control nanobots and these nanobots will be used in the body for the purpose of killing cancer tumours. And al-Rifaie and Aber have used swarm learning to help locate tumours.
- Swarm learning can also be used in the field of data mining and cluster analysis.
Here in the article, we have seen the problems with the traditional centralized learning techniques and how can we overcome these drawbacks or the problem of the centralized learning techniques using the decentralized learning techniques. Swarm learning is a type of decentralized learning technique that has two major approaches as Ant Colony Optimization(ACO) and Particle Swarm Optimization(PSO). We have also discussed these parts along with applications of Swarm learning.