  Robust machine learning (ML) models can be developed by leveraging large
volumes of data and distributing the computational tasks across numerous
devices or servers. Federated learning (FL) is a technique in the realm of ML
that facilitates this goal by utilizing cloud infrastructure to enable
collaborative model training among a network of decentralized devices. Beyond
distributing the computational load, FL targets the resolution of privacy
issues and the reduction of communication costs simultaneously. To protect user
privacy, FL requires users to send model updates rather than transmitting large
quantities of raw and potentially confidential data. Specifically, individuals
train ML models locally using their own data and then upload the results in the
form of weights and gradients to the cloud for aggregation into the global
model. This strategy is also advantageous in environments with limited
bandwidth or high communication costs, as it prevents the transmission of large
data volumes. With the increasing volume of data and rising privacy concerns,
alongside the emergence of large-scale ML models like Large Language Models
(LLMs), FL presents itself as a timely and relevant solution. It is therefore
essential to review current FL algorithms to guide future research that meets
the rapidly evolving ML demands. This survey provides a comprehensive analysis
and comparison of the most recent FL algorithms, evaluating them on various
fronts including mathematical frameworks, privacy protection, resource
allocation, and applications. Beyond summarizing existing FL methods, this
survey identifies potential gaps, open areas, and future challenges based on
the performance reports and algorithms used in recent studies. This survey
enables researchers to readily identify existing limitations in the FL field
for further exploration.
