Federated Learning: Attacks and Defenses, Rewards, Energy Efficiency: Past, Present and Future

Federated Learning (FL) was first introduced as an idea by Google in 2016, in which multiple devices jointly train a machine learning model without sharing their data under the supervision of a central server. This offers big opportunities in critical areas like healthcare, industry, and finance, where sharing information with other organizations’ devices is completely prohibited. The combination of Federated Learning with Blockchain technology has led to the so-called Blockchain Federated learning (B.F.L.) which operates in a distributed manner and offers enhanced trust, improved security and privacy, improved traceability and immutability and at the same time enables dataset monetization through tokenization. Unfortunately, vulnerabilities of the blockchain-based solutions have been identified while the implementation of blockchain introduces significant energy consumption issues. There are many solutions that also offer personalized ideas and uses. In the field of security, solutions such as security against model-poisoning backdoor assaults with poles and modified algorithms are proposed. Defense systems that identify hostile devices, Against Phishing and other social engineering attack mechanisms that could threaten current security systems after careful comparison of mutual systems. In a federated learning system built on blockchain, the design of reward mechanisms plays a crucial role in incentivizing active participation. We can use tokens for rewards or other cryptocurrency methods for rewards to a federated learning system. Smart Contracts combined with proof of stake with performance-based rewards or (and) value of data contribution. Some of them use games or game theory-inspired mechanisms with unlimited uses even in other applications like games. All of the above is useless if the energy consumption exceeds the cost of implementing a system. Thus, all of the above is combined with algorithms that make simple or more complex hardware and software adjustments. Heterogeneous data fusion methods, energy consumption models, bandwidth, and controls transmission power try to solve the optimization problems to reduce energy consumption, including communication and compute energy. New technologies such as quantum computing with its advantages such as speed and the ability to solve problems that classical computers cannot solve, their multidimensional nature, analyze large data sets more efficiently than classical artificial intelligence counterparts and the later maturity of a technology that is now expensive will provide solutions in areas such as cryptography, security and why not in energy autonomy. The human brain and an emerging technology can provide solutions to all of the above solutions due to the brain's decentralized nature, built-in reward mechanism, negligible energy use, and really high processing power In this paper we attempt to survey the currently identified threats, attacks and defenses, the rewards and the energy efficiency issues of BFL in order to guide the researchers and the designers of FL based solution to adopt the most appropriate of each application approach.