A Survey on Vulnerability of Federated Learning: A Learning Algorithm
  Perspective
Xianghua Xie Chen Hu Hanchi Ren Jingjing Deng
This review paper takes a comprehensive look at malicious attacks against FL,
categorizing them from new perspectives on attack origins and targets, and
providing insights into their methodology and impact. In this survey, we focus
on threat models targeting the learning process of FL systems. Based on the
source and target of the attack, we categorize existing threat models into four
types, Data to Model (D2M), Model to Data (M2D), Model to Model (M2M) and
composite attacks. For each attack type, we discuss the defense strategies
proposed, highlighting their effectiveness, assumptions and potential areas for
improvement. Defense strategies have evolved from using a singular metric to
excluding malicious clients, to employing a multifaceted approach examining
client models at various phases. In this survey paper, our research indicates
that the to-learn data, the learning gradients, and the learned model at
different stages all can be manipulated to initiate malicious attacks that
range from undermining model performance, reconstructing private local data,
and to inserting backdoors. We have also seen these threat are becoming more
insidious. While earlier studies typically amplified malicious gradients,
recent endeavors subtly alter the least significant weights in local models to
bypass defense measures. This literature review provides a holistic
understanding of the current FL threat landscape and highlights the importance
of developing robust, efficient, and privacy-preserving defenses to ensure the
safe and trusted adoption of FL in real-world applications.