We present a novel privacy-preserving model aggregation for asynchronous
federated learning, named PPA-AFL that removes the restriction of synchronous
aggregation of local model updates in federated learning, while enabling the
protection of the local model updates against the server. In PPA-AFL, clients
can proactive decide when to engage in the training process, and sends local
model updates to the server when the updates are available. Thus, it is not
necessary to keep synchronicity with other clients. To safeguard client updates
and facilitate local model aggregation, we employ Paillier encryption for local
update encryption and support homomorphic aggregation. Furthermore, secret
sharing is utilized to enable the sharing of decryption keys and facilitate
privacy-preserving asynchronous aggregation. As a result, the server remains
unable to gain any information about the local updates while asynchronously
aggregating to produce the global model. We demonstrate the efficacy of our
proposed PPA-AFL framework through comprehensive complexity analysis and
extensive experiments on a prototype implementation, highlighting its potential
for practical adoption in privacy-sensitive asynchronous federated learning
scenarios