  Federated learning combines local updates from clients to produce a global
model, which is susceptible to poisoning attacks. Most previous defense
strategies relied on vectors derived from projections of local updates on a
Euclidean space; however, these methods fail to accurately represent the
functionality and structure of local models, resulting in inconsistent
performance. Here, we present a new paradigm to defend against poisoning
attacks in federated learning using functional mappings of local models based
on intermediate outputs. Experiments show that our mechanism is robust under a
broad range of computing conditions and advanced attack scenarios, enabling
safer collaboration among data-sensitive participants via federated learning.
