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1. ELSA: Secure Aggregation for Federated Learning with Malicious Actors

   Rathee, Mayank ; Shen, Conghao ; Wagh, Sameer ; Popa, Raluca Ada ( January 2023 , Proceedings of the IEEE Symposium on Security and Privacy)

   Federated learning (FL) is an increasingly popular approach for machine learning (ML) in cases where the training dataset is highly distributed. Clients perform local training on their datasets and the updates are then aggregated into the global model. Existing protocols for aggregation are either inefficient, or don’t consider the case of malicious actors in the system. This is a major barrier in making FL an ideal solution for privacy-sensitive ML applications. We present ELSA, a secure aggregation protocol for FL, which breaks this barrier - it is efficient and addresses the existence of malicious actors at the core of its design. Similar to prior work on Prio and Prio+, ELSA provides a novel secure aggregation protocol built out of distributed trust across two servers that keeps individual client updates private as long as one server is honest, defends against malicious clients, and is efficient end-to-end. Compared to prior works, the distinguishing theme in ELSA is that instead of the servers generating cryptographic correlations interactively, the clients act as untrusted dealers of these correlations without compromising the protocol’s security. This leads to a much faster protocol while also achieving stronger security at that efficiency compared to prior work. We introduce new techniques that retain privacy even when a server is malicious at a small added cost of 7-25% in runtime with negligible increase in communication over the case of semi-honest server. Our work improves end-to-end runtime over prior work with similar security guarantees by big margins - single-aggregator RoFL by up to 305x (for the models we consider), and distributed trust Prio by up to 8x. 

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2. ELSA: Secure Aggregation for Federated Learning with Malicious Actors

   Rathee, Mayank ; Shen, Conghao ; Wagh, Sameer ; Popa, Raluca Ada ( January 2023 , Proceedings of the IEEE Symposium on Security and Privacy)

   Federated learning (FL) is an increasingly popular approach for machine learning (ML) when the training dataset is highly distributed. Clients perform local training on their datasets and the updates are then aggregated into the global model. Existing protocols for aggregation are either inefficient or don’t consider the case of malicious actors in the system. This is a major barrier to making FL an ideal solution for privacy-sensitive ML applications. In this talk, I will present ELSA, a secure aggregation protocol for FL that breaks this barrier - it is efficient and addresses the existence of malicious actors (clients + servers) at the core of its design. Similar to prior work Prio and Prio+, ELSA provides a novel secure aggregation protocol built out of distributed trust across two servers that keeps individual client updates private as long as one server is honest, defends against malicious clients, and is efficient end-to-end. Compared to prior works, the distinguishing theme in ELSA is that instead of the servers generating cryptographic correlations interactively, the clients act as untrusted dealers of these correlations without compromising the protocol’s security. This leads to a much faster protocol while also achieving stronger security at that efficiency compared to prior work. We introduce new techniques that retain privacy even when a server is malicious at a small added cost of 7-25% in runtime with a negligible increase in communication over the case of a semi-honest server. ELSA improves end-to-end runtime over prior work with similar security guarantees by big margins - single-aggregator RoFL by up to 305x (for the models we consider), and distributed-trust Prio by up to 8x (with up to 16x faster server-side protocol). Additionally, ELSA can be run in a bandwidth-saver mode for clients who are geographically bandwidth-constrained - an important property that is missing from prior works. 

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3. Latency-Aware Semi-Synchronous Client Selection and Model Aggregation for Wireless Federated Learning

   https://doi.org/10.3390/fi15110352  

   Yu, Liangkun ; Sun, Xiang ; Albelaihi, Rana ; Yi, Chen ( November 2023 , Future Internet)

   Federated learning (FL) is a collaborative machine-learning (ML) framework particularly suited for ML models requiring numerous training samples, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Random Forest, in the context of various applications, e.g., next-word prediction and eHealth. FL involves various clients participating in the training process by uploading their local models to an FL server in each global iteration. The server aggregates these models to update a global model. The traditional FL process may encounter bottlenecks, known as the straggler problem, where slower clients delay the overall training time. This paper introduces the Latency-awarE Semi-synchronous client Selection and mOdel aggregation for federated learNing (LESSON) method. LESSON allows clients to participate at different frequencies: faster clients contribute more frequently, therefore mitigating the straggler problem and expediting convergence. Moreover, LESSON provides a tunable trade-off between model accuracy and convergence rate by setting varying deadlines. Simulation results show that LESSON outperforms two baseline methods, namely FedAvg and FedCS, in terms of convergence speed and maintains higher model accuracy compared to FedCS.

    

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4. How to learn collaboratively - Federated learning to peer-to-peer learning and what's at stake.

   Sharma, Atul ; Zhao, Joshua ; Chen, Wei ; Qiu, Qiang ; Bagchi, Saurabh ; Chaterji, Somali ( June 2023 , 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), “Disrupt 23: Disruptive Ideas and New Interdisciplinary Results” Track)

   Standard ML relies on training using a centrally collected dataset, while collaborative learning techniques such as Federated Learning (FL) enable data to remain decentralized at client locations. In FL, a central server coordinates the training process, reducing computation and communication expenses for clients. However, this centralization can lead to server congestion and heightened risk of malicious activity or data privacy breaches. In contrast, Peer-to-Peer Learning (P2PL) is a fully decentralized system where nodes manage both local training and aggregation tasks. While P2PL promotes privacy by eliminating the need to trust a single node, it also results in increased computation and communication costs, along with potential difficulties in achieving consensus among nodes. To address the limitations of both FL and P2PL, we propose a hybrid approach called Hubs-and-Spokes Learning (HSL). In HSL, hubs function similarly to FL servers, maintaining consensus but exerting less control over spokes. This paper argues that HSL’s design allows for greater availability and privacy than FL, while reducing computation and communication costs compared to P2PL. Additionally, HSL maintains consensus and integrity in the learning process. 

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5. Differentially Private Federated Learning with Shuffling and Client Self-Sampling

   Girgis, Antonious M ; Data, Deepesh ; and Diggavi, Suhas. ( July 2021 , IEEE International Symposium on Information Theory (ISIT))

   This paper studies a distributed optimization problem in the federated learning (FL) framework under differential privacy constraints, whereby a set of clients having local samples are connected to an untrusted server, who wants to learn a global model while preserving the privacy of clients’ local datasets. We propose a new client sampling called self-sampling that reflects the random availability of clients in the learning process in FL. We analyze the differential privacy of the SGD with client self-sampling by composing amplification by sub-sampling along with amplification by shuffling. Furthermore, we analyze the convergence of the proposed SGD algorithm showing that we can get a reasonable learning performance while preserving the privacy of clients’ data even with client self-sampling. 

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