PointerTensor

PointerTensor is a proxy object used in distributed and privacy-preserving machine learning frameworks, most commonly within PySyft, to reference a tensor that resides on a remote worker. It enables computations to be described and executed without moving the underlying data to the local environment, supporting federated and confidential learning scenarios.

How it works

A PointerTensor stores a reference to a remote tensor, including the remote location (the worker where the data lives) and the identifier of the tensor on that remote location. Operations invoked on a PointerTensor are forwarded to the remote worker where the actual data resides, and the results are typically returned as new pointers or, in some cases, as a local result after a gather operation. This enables lazy execution and network-based computation, reducing data movement.

Creation and use

PointerTensors are typically created by sending a local tensor to a remote worker via a send operation. The local side then holds a PointerTensor that points to the remote tensor. In federated setups, multiple pointers can reference data across different workers, allowing secure, distributed computation where the data owners retain control over their data.

Operations and data flow

Operations on PointerTensors are translated into remote commands. The actual arithmetic or tensor manipulations occur on the remote worker, and the outcome may be returned as a new PointerTensor or retrieved locally through explicit gather/get operations. This mechanism supports privacy-preserving workflows, such as performing model training or inference on remote data without exposing raw values.

Considerations

Using PointerTensors introduces network latency and requires secure communication channels. Proper risk management and cryptographic protocols are essential for protecting data privacy in distributed setups.

See also

PySyft, federated learning, remote tensors, secure computation.