On the Fragility of Contribution Score Computation in Federated Learning

Balazs Pejo, Marcell Frank, Krisztian Varga, Peter Veliczky, Gergely Biczok

Abstract:

This paper investigates the fragility of contribu-
tion evaluation in federated learning, a critical mechanism
for ensuring fairness and incentivizing participation. We argue
that contribution scores are susceptible to significant distortions
from two fundamental perspectives: architectural sensitivity and
intentional manipulation. First, we explore how different model
aggregation methods impact these scores. While most research
works assume a basic averaging approach, we demonstrate
that advanced techniques, including those designed to handle
unreliable or diverse clients, can unintentionally yet significantly
alter the final scores. Second, we examine the threat posed
by poisoning attacks, where malicious participants strategically
manipulate their model updates to either inflate their own contri-
bution scores or reduce others’. Through extensive experiments
across diverse datasets and model architectures, implemented
within the Flower framework, we rigorously show that both the
choice of aggregation method and the presence of attackers can
substantially skew contribution scores, highlighting the need for
more robust contribution evaluation schemes.